From 3dd58203bb55d8f9f5489c881c6c192be36e20a6 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 05:29:58 +0800
Subject: [PATCH 01/41] train successfully

---
 README.md                                     |   9 +-
 assets/sample_result_image_1.jpg              | Bin 0 -> 13986 bytes
 nbs/export.ipynb                              |  90 ++++++++++++++++++
 scripts/live_inference.py                     |   6 +-
 scripts/train.py                              |  23 ++---
 .../deim_dfine/deim_hgnetv2_n_coco.yml        |   2 +-
 src/deimkit/engine/data/dataset/coco_eval.py  |  10 +-
 src/deimkit/engine/deim/box_ops.py            |   7 +-
 src/deimkit/engine/deim/dfine_decoder.py      |  49 +++++-----
 src/deimkit/engine/deim/dfine_utils.py        |  14 ++-
 src/deimkit/engine/deim/hybrid_encoder.py     |   5 +-
 src/deimkit/engine/deim/postprocessor.py      |  21 ++--
 12 files changed, 168 insertions(+), 68 deletions(-)
 create mode 100644 assets/sample_result_image_1.jpg
 create mode 100644 nbs/export.ipynb

diff --git a/README.md b/README.md
index ee5d1b66..12e0b736 100644
--- a/README.md
+++ b/README.md
@@ -39,12 +39,7 @@
 ## Installation
 
 ### Using pip
-Install [torch](https://pytorch.org/get-started/locally/) and torchvision as a pre-requisite.
-
-## Installation
-
-### Using pip
-Install [torch](https://pytorch.org/get-started/locally/) and torchvision as a pre-requisite.
+If you're installing using pip, install [torch](https://pytorch.org/get-started/locally/) and torchvision as a pre-requisite.
 
 Next, install the package.
 Bleeding edge version
@@ -291,7 +286,7 @@ python scripts/live_inference.py
 ```
 The following is a demo of image inference
 
-![image](assets/sample_result_image.jpg)
+![image](assets/sample_result_image_1.jpg)
 
 > [!TIP]
 > If you are using Pixi, you can run the live inference script with the following command with the same arguments as above.
diff --git a/assets/sample_result_image_1.jpg b/assets/sample_result_image_1.jpg
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diff --git a/nbs/export.ipynb b/nbs/export.ipynb
new file mode 100644
index 00000000..241a4337
--- /dev/null
+++ b/nbs/export.ipynb
@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-03-31 04:01:06.942\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:07.256\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:07.380\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:07.381\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m96\u001b[0m - \u001b[1mUsing input shape from config: (1, 3, 640, 640)\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:07.387\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m108\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded stage1 B0 HGNetV2 from local file.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dnth/Desktop/DEIMKit/src/deimkit/engine/deim/dfine_decoder.py:646: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  if memory.shape[0] > 1:\n",
+      "/home/dnth/Desktop/DEIMKit/src/deimkit/engine/deim/dfine_decoder.py:129: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  if reference_points.shape[-1] == 2:\n",
+      "/home/dnth/Desktop/DEIMKit/src/deimkit/engine/deim/dfine_decoder.py:133: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  elif reference_points.shape[-1] == 4:\n",
+      "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/_internal/jit_utils.py:308: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
+      "  _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)\n",
+      "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/utils.py:657: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
+      "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
+      "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/utils.py:1127: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
+      "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
+      "\u001b[32m2025-03-31 04:01:08.747\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m123\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:08.839\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mONNX model validation successful\u001b[0m\n",
+      "\u001b[32m2025-03-31 04:01:11.256\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m180\u001b[0m - \u001b[1mONNX model simplification successful\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deimkit.exporter import Exporter\n",
+    "from deimkit.config import Config\n",
+    "\n",
+    "config = Config(\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/config.yml\")\n",
+    "exporter = Exporter(config)\n",
+    "\n",
+    "output_path = exporter.to_onnx(\n",
+    "    checkpoint_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\",\n",
+    "    output_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cuda",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/live_inference.py b/scripts/live_inference.py
index f29c5477..929db73b 100644
--- a/scripts/live_inference.py
+++ b/scripts/live_inference.py
@@ -57,7 +57,7 @@ def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4, class_names=
         ratio = ratios[i]
         pad_w, pad_h = paddings[i]
 
-        for lbl, bb in zip(lab, box):
+        for j, (lbl, bb) in enumerate(zip(lab, box)):
             # Get color for this class
             class_idx = int(lbl)
             color = colors[class_idx % len(colors)]
@@ -78,9 +78,9 @@ def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4, class_names=
 
             # Use class name if available, otherwise use class index
             if class_names and class_idx < len(class_names):
-                label_text = f"{class_names[class_idx]} {scr[lab == lbl][0]:.2f}"
+                label_text = f"{class_names[class_idx]} {scr[j]:.2f}"
             else:
-                label_text = f"Class {class_idx} {scr[lab == lbl][0]:.2f}"
+                label_text = f"Class {class_idx} {scr[j]:.2f}"
 
             # Draw text background
             text_size = draw.textbbox((0, 0), label_text, font=None)
diff --git a/scripts/train.py b/scripts/train.py
index 46fc62a3..83bd6935 100644
--- a/scripts/train.py
+++ b/scripts/train.py
@@ -1,22 +1,23 @@
-from deimkit import Trainer, Config, configure_dataset, configure_model
+from deimkit import Config, Trainer, configure_dataset, configure_model
 
 conf = Config.from_model_name("deim_hgnetv2_s")
 
-conf = configure_model(conf, num_queries=100)
+conf = configure_model(conf, num_queries=100, freeze_at=0, pretrained=True)
 
 conf = configure_dataset(
     config=conf,
     image_size=(640, 640),
-    train_ann_file="/home/dnth/Desktop/DEIMKit/dataset_collections/aquarium-combined-gjvb.v1i.coco/train/_annotations.coco.json",
-    train_img_folder="/home/dnth/Desktop/DEIMKit/dataset_collections/aquarium-combined-gjvb.v1i.coco/train",
-    val_ann_file="/home/dnth/Desktop/DEIMKit/dataset_collections/aquarium-combined-gjvb.v1i.coco/valid/_annotations.coco.json",
-    val_img_folder="/home/dnth/Desktop/DEIMKit/dataset_collections/aquarium-combined-gjvb.v1i.coco/valid",
-    train_batch_size=16,
-    val_batch_size=16,
-    num_classes=8,
-    output_dir="./outputs/aquarium/deim_hgnetv2_s_30ep_640px_num_queries_100_no_aug_epoch_15",
+    train_ann_file="/home/dnth/Desktop/DEIMKit/dataset_collections/Rock Paper Scissors SXSW.v14i.coco/train/_annotations.coco.json",
+    train_img_folder="/home/dnth/Desktop/DEIMKit/dataset_collections/Rock Paper Scissors SXSW.v14i.coco/train",
+    val_ann_file="/home/dnth/Desktop/DEIMKit/dataset_collections/Rock Paper Scissors SXSW.v14i.coco/valid/_annotations.coco.json",
+    val_img_folder="/home/dnth/Desktop/DEIMKit/dataset_collections/Rock Paper Scissors SXSW.v14i.coco/valid",
+    train_batch_size=20,
+    val_batch_size=20,
+    num_classes=4,
+    remap_mscoco=False,
+    output_dir="./outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100",
 )
 
 trainer = Trainer(conf)
 
-trainer.fit(epochs=30, save_best_only=True, no_aug_epoch=15)
+trainer.fit(epochs=30, save_best_only=True)
diff --git a/src/deimkit/configs/deim_dfine/deim_hgnetv2_n_coco.yml b/src/deimkit/configs/deim_dfine/deim_hgnetv2_n_coco.yml
index 62db245d..a5c265a5 100644
--- a/src/deimkit/configs/deim_dfine/deim_hgnetv2_n_coco.yml
+++ b/src/deimkit/configs/deim_dfine/deim_hgnetv2_n_coco.yml
@@ -27,7 +27,7 @@ optimizer:
 epoches: 160 # 148 + 12
 
 ## Our LR-Scheduler
-flat_epoch: 7800    # 4 + epoch // 2, e.g., 40 = 4 + 72 / 2
+flat_epoch: 78    # 4 + epoch // 2, e.g., 40 = 4 + 72 / 2
 no_aug_epoch: 12
 lr_gamma: 1.0
 
diff --git a/src/deimkit/engine/data/dataset/coco_eval.py b/src/deimkit/engine/data/dataset/coco_eval.py
index 75f6bd8d..a5fd366b 100644
--- a/src/deimkit/engine/data/dataset/coco_eval.py
+++ b/src/deimkit/engine/data/dataset/coco_eval.py
@@ -96,13 +96,19 @@ def prepare_for_coco_detection(self, predictions):
             boxes = prediction["boxes"]
             boxes = convert_to_xywh(boxes).tolist()
             scores = prediction["scores"].tolist()
-            labels = prediction["labels"].tolist()
+            labels = prediction["labels"]
+            
+            # Handle different label formats
+            if isinstance(labels, torch.Tensor):
+                labels = labels.flatten().tolist()
+            elif isinstance(labels, list) and isinstance(labels[0], list):
+                labels = [l[0] if isinstance(l, list) else l for l in labels]
 
             coco_results.extend(
                 [
                     {
                         "image_id": original_id,
-                        "category_id": labels[k],
+                        "category_id": int(labels[k]),
                         "bbox": box,
                         "score": scores[k],
                     }
diff --git a/src/deimkit/engine/deim/box_ops.py b/src/deimkit/engine/deim/box_ops.py
index ede1b324..44bae964 100644
--- a/src/deimkit/engine/deim/box_ops.py
+++ b/src/deimkit/engine/deim/box_ops.py
@@ -16,10 +16,13 @@ def box_cxcywh_to_xyxy(x):
 
 
 def box_xyxy_to_cxcywh(x: Tensor) -> Tensor:
-    x0, y0, x1, y1 = x.unbind(-1)
+    x0 = x[..., 0:1]
+    y0 = x[..., 1:2]
+    x1 = x[..., 2:3]
+    y1 = x[..., 3:4]
     b = [(x0 + x1) / 2, (y0 + y1) / 2,
          (x1 - x0), (y1 - y0)]
-    return torch.stack(b, dim=-1)
+    return torch.cat(b, dim=-1)
 
 
 # modified from torchvision to also return the union
diff --git a/src/deimkit/engine/deim/dfine_decoder.py b/src/deimkit/engine/deim/dfine_decoder.py
index c791d8b9..9741b3a6 100644
--- a/src/deimkit/engine/deim/dfine_decoder.py
+++ b/src/deimkit/engine/deim/dfine_decoder.py
@@ -261,11 +261,11 @@ def __init__(self, reg_max=32):
         self.reg_max = reg_max
 
     def forward(self, x, project):
-        shape = x.shape
-        x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
-        x = F.linear(x, project.to(x.device)).reshape(-1, 4)
-        return x.reshape(list(shape[:-1]) + [-1])
-
+        n,c,h = x.shape
+        b = n*c*h // (self.reg_max + 1)
+        x = F.softmax(x.reshape([b, self.reg_max + 1]), dim=1)
+        x = F.linear(x, project.to(x.device))
+        return x.reshape([n,c,4])
 
 class LQE(nn.Module):
     def __init__(self, k, hidden_dim, num_layers, reg_max, act='relu'):
@@ -393,8 +393,10 @@ def forward(self,
             ref_points_detach = inter_ref_bbox.detach()
             output_detach = output.detach()
 
-        return torch.stack(dec_out_bboxes), torch.stack(dec_out_logits), \
-               torch.stack(dec_out_pred_corners), torch.stack(dec_out_refs), pre_bboxes, pre_scores
+        if len(dec_out_logits) > 1:
+            return torch.stack(dec_out_bboxes), torch.stack(dec_out_logits), torch.stack(dec_out_pred_corners), torch.stack(dec_out_refs), pre_bboxes, pre_scores
+        else:
+            return dec_out_bboxes[0], dec_out_logits[0], dec_out_pred_corners[0], dec_out_refs[0], pre_bboxes, pre_scores
 
 
 @register()
@@ -593,7 +595,8 @@ def _get_encoder_input(self, feats: List[torch.Tensor]):
         for i, feat in enumerate(proj_feats):
             _, _, h, w = feat.shape
             # [b, c, h, w] -> [b, h*w, c]
-            feat_flatten.append(feat.flatten(2).permute(0, 2, 1))
+            n,c,h,w = feat.shape
+            feat_flatten.append(feat.reshape([n,c,h*w]).permute(0, 2, 1))
             # [num_levels, 2]
             spatial_shapes.append([h, w])
 
@@ -625,7 +628,6 @@ def _generate_anchors(self,
         valid_mask = ((anchors > self.eps) * (anchors < 1 - self.eps)).all(-1, keepdim=True)
         anchors = torch.log(anchors / (1 - anchors))
         anchors = torch.where(valid_mask, anchors, torch.inf)
-
         return anchors, valid_mask
 
 
@@ -691,14 +693,11 @@ def _select_topk(self, memory: torch.Tensor, outputs_logits: torch.Tensor, outpu
 
         topk_ind: torch.Tensor
 
-        topk_anchors = outputs_anchors_unact.gather(dim=1, \
-            index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_anchors_unact.shape[-1]))
-
-        topk_logits = outputs_logits.gather(dim=1, \
-            index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_logits.shape[-1])) if self.training else None
-
-        topk_memory = memory.gather(dim=1, \
-            index=topk_ind.unsqueeze(-1).repeat(1, 1, memory.shape[-1]))
+        n = topk_ind.shape[0]
+        outputs_anchors_unact = outputs_anchors_unact * torch.ones([n, 1, 1], device=outputs_anchors_unact.device)
+        topk_anchors = outputs_anchors_unact.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_anchors_unact.shape[-1]))
+        topk_logits = outputs_logits.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_logits.shape[-1])) if self.training else None
+        topk_memory = memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, memory.shape[-1]))
 
         return topk_memory, topk_logits, topk_anchors
 
@@ -752,21 +751,21 @@ def forward(self, feats, targets=None):
 
 
         if self.training:
-            out = {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1], 'pred_corners': out_corners[-1],
-                   'ref_points': out_refs[-1], 'up': self.up, 'reg_scale': self.reg_scale}
+            out = {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1], 'pred_corners': out_corners[-1], 'ref_points': out_refs[-1], 'up': self.up, 'reg_scale': self.reg_scale}
         else:
-            out = {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1]}
+            if len(out_logits.shape) == 4:
+                out = {'pred_logits': out_logits[-1], 'pred_boxes': out_bboxes[-1]}
+            else:
+                out = {'pred_logits': out_logits, 'pred_boxes': out_bboxes}
 
         if self.training and self.aux_loss:
-            out['aux_outputs'] = self._set_aux_loss2(out_logits[:-1], out_bboxes[:-1], out_corners[:-1], out_refs[:-1],
-                                                     out_corners[-1], out_logits[-1])
+            out['aux_outputs'] = self._set_aux_loss2(out_logits[:-1], out_bboxes[:-1], out_corners[:-1], out_refs[:-1], out_corners[-1], out_logits[-1])
             out['enc_aux_outputs'] = self._set_aux_loss(enc_topk_logits_list, enc_topk_bboxes_list)
             out['pre_outputs'] = {'pred_logits': pre_logits, 'pred_boxes': pre_bboxes}
             out['enc_meta'] = {'class_agnostic': self.query_select_method == 'agnostic'}
 
             if dn_meta is not None:
-                out['dn_outputs'] = self._set_aux_loss2(dn_out_logits, dn_out_bboxes, dn_out_corners, dn_out_refs,
-                                                        dn_out_corners[-1], dn_out_logits[-1])
+                out['dn_outputs'] = self._set_aux_loss2(dn_out_logits, dn_out_bboxes, dn_out_corners, dn_out_refs, dn_out_corners[-1], dn_out_logits[-1])
                 out['dn_pre_outputs'] = {'pred_logits': dn_pre_logits, 'pred_boxes': dn_pre_bboxes}
                 out['dn_meta'] = dn_meta
 
@@ -789,4 +788,4 @@ def _set_aux_loss2(self, outputs_class, outputs_coord, outputs_corners, outputs_
         # as a dict having both a Tensor and a list.
         return [{'pred_logits': a, 'pred_boxes': b, 'pred_corners': c, 'ref_points': d,
                      'teacher_corners': teacher_corners, 'teacher_logits': teacher_logits}
-                for a, b, c, d in zip(outputs_class, outputs_coord, outputs_corners, outputs_ref)]
+                for a, b, c, d in zip(outputs_class, outputs_coord, outputs_corners, outputs_ref)]
\ No newline at end of file
diff --git a/src/deimkit/engine/deim/dfine_utils.py b/src/deimkit/engine/deim/dfine_utils.py
index c0864e20..a54d88ad 100644
--- a/src/deimkit/engine/deim/dfine_utils.py
+++ b/src/deimkit/engine/deim/dfine_utils.py
@@ -119,13 +119,11 @@ def distance2bbox(points, distance, reg_scale):
         Tensor: Bounding boxes in (N, 4) or (B, N, 4) format [cx, cy, w, h].
     """
     reg_scale = abs(reg_scale)
-    x1 = points[..., 0] - (0.5 * reg_scale + distance[..., 0]) * (points[..., 2] / reg_scale)
-    y1 = points[..., 1] - (0.5 * reg_scale + distance[..., 1]) * (points[..., 3] / reg_scale)
-    x2 = points[..., 0] + (0.5 * reg_scale + distance[..., 2]) * (points[..., 2] / reg_scale)
-    y2 = points[..., 1] + (0.5 * reg_scale + distance[..., 3]) * (points[..., 3] / reg_scale)
-
-    bboxes = torch.stack([x1, y1, x2, y2], -1)
-
+    x1 = points[..., 0:1] - (0.5 * reg_scale + distance[..., 0:1]) * (points[..., 2:3] / reg_scale)
+    y1 = points[..., 1:2] - (0.5 * reg_scale + distance[..., 1:2]) * (points[..., 3:4] / reg_scale)
+    x2 = points[..., 0:1] + (0.5 * reg_scale + distance[..., 2:3]) * (points[..., 2:3] / reg_scale)
+    y2 = points[..., 1:2] + (0.5 * reg_scale + distance[..., 3:4]) * (points[..., 3:4] / reg_scale)
+    bboxes = torch.cat([x1, y1, x2, y2], -1)
     return box_xyxy_to_cxcywh(bboxes)
 
 
@@ -153,4 +151,4 @@ def bbox2distance(points, bbox, reg_max, reg_scale, up, eps=0.1):
     four_lens, weight_right, weight_left = translate_gt(four_lens, reg_max, reg_scale, up)
     if reg_max is not None:
         four_lens = four_lens.clamp(min=0, max=reg_max-eps)
-    return four_lens.reshape(-1).detach(), weight_right.detach(), weight_left.detach()
+    return four_lens.reshape(-1).detach(), weight_right.detach(), weight_left.detach()
\ No newline at end of file
diff --git a/src/deimkit/engine/deim/hybrid_encoder.py b/src/deimkit/engine/deim/hybrid_encoder.py
index 14b49159..5ed36724 100644
--- a/src/deimkit/engine/deim/hybrid_encoder.py
+++ b/src/deimkit/engine/deim/hybrid_encoder.py
@@ -401,7 +401,8 @@ def forward(self, feats):
             for i, enc_ind in enumerate(self.use_encoder_idx):
                 h, w = proj_feats[enc_ind].shape[2:]
                 # flatten [B, C, H, W] to [B, HxW, C]
-                src_flatten = proj_feats[enc_ind].flatten(2).permute(0, 2, 1)
+                n,c,h,w = proj_feats[enc_ind].shape
+                src_flatten = proj_feats[enc_ind].reshape([n,c,h*w]).permute(0, 2, 1)
                 if self.training or self.eval_spatial_size is None:
                     pos_embed = self.build_2d_sincos_position_embedding(
                         w, h, self.hidden_dim, self.pe_temperature).to(src_flatten.device)
@@ -430,4 +431,4 @@ def forward(self, feats):
             out = self.pan_blocks[idx](torch.concat([downsample_feat, feat_height], dim=1))
             outs.append(out)
 
-        return outs
+        return outs
\ No newline at end of file
diff --git a/src/deimkit/engine/deim/postprocessor.py b/src/deimkit/engine/deim/postprocessor.py
index ff66842b..dee4c0a5 100644
--- a/src/deimkit/engine/deim/postprocessor.py
+++ b/src/deimkit/engine/deim/postprocessor.py
@@ -47,21 +47,23 @@ def extra_repr(self) -> str:
         return f'use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}'
 
     # def forward(self, outputs, orig_target_sizes):
-    def forward(self, outputs, orig_target_sizes: torch.Tensor):
+    def forward(self, outputs, orig_target_sizes: torch.Tensor=None):
         logits, boxes = outputs['pred_logits'], outputs['pred_boxes']
         # orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
 
         bbox_pred = torchvision.ops.box_convert(boxes, in_fmt='cxcywh', out_fmt='xyxy')
-        bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1)
+        if orig_target_sizes is not None:
+            bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1)
 
         if self.use_focal_loss:
             scores = F.sigmoid(logits)
             scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1)
-            # TODO for older tensorrt
-            # labels = index % self.num_classes
+            if orig_target_sizes is None:
+                scores = scores.unsqueeze(-1)
+            index = index.unsqueeze(-1)
             labels = mod(index, self.num_classes)
             index = index // self.num_classes
-            boxes = bbox_pred.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, bbox_pred.shape[-1]))
+            boxes = bbox_pred.gather(dim=1, index=index.repeat(1, 1, bbox_pred.shape[-1]))
 
         else:
             scores = F.softmax(logits)[:, :, :-1]
@@ -73,7 +75,10 @@ def forward(self, outputs, orig_target_sizes: torch.Tensor):
 
         # TODO for onnx export
         if self.deploy_mode:
-            return labels, boxes, scores
+            if orig_target_sizes is not None:
+                return labels, boxes, scores
+            else:
+                return torch.cat([labels, boxes, scores], dim=2)
 
         # TODO
         if self.remap_mscoco_category:
@@ -83,6 +88,8 @@ def forward(self, outputs, orig_target_sizes: torch.Tensor):
 
         results = []
         for lab, box, sco in zip(labels, boxes, scores):
+            # Ensure labels are flattened single values
+            lab = lab.flatten() if isinstance(lab, torch.Tensor) else lab
             result = dict(labels=lab, boxes=box, scores=sco)
             results.append(result)
 
@@ -92,4 +99,4 @@ def forward(self, outputs, orig_target_sizes: torch.Tensor):
     def deploy(self, ):
         self.eval()
         self.deploy_mode = True
-        return self
+        return self
\ No newline at end of file

From 82ff375253956a9905103080b70c1ba2758fc542 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 05:48:47 +0800
Subject: [PATCH 02/41] update exporter

---
 nbs/export.ipynb        |  26 +++--
 src/deimkit/exporter.py | 210 ++++++++++++++++++++++++++++++++--------
 2 files changed, 185 insertions(+), 51 deletions(-)

diff --git a/nbs/export.ipynb b/nbs/export.ipynb
index 241a4337..14d29627 100644
--- a/nbs/export.ipynb
+++ b/nbs/export.ipynb
@@ -9,11 +9,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-31 04:01:06.942\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:07.256\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m53\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:07.380\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m75\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:07.381\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m96\u001b[0m - \u001b[1mUsing input shape from config: (1, 3, 640, 640)\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:07.387\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m108\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
+      "\u001b[32m2025-03-31 05:47:54.978\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mUsing device: cpu\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:54.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m71\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:55.301\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m76\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:55.436\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:55.437\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mUsing input shape from config: (1, 3, 640, 640)\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:55.443\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mUsing dynamic axes: {'images': {0: 'N'}, 'orig_target_sizes': {0: 'N'}, 'labels': {0: 'N'}, 'boxes': {0: 'N'}, 'scores': {0: 'N'}}\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:55.443\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
      ]
     },
     {
@@ -39,9 +41,10 @@
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
       "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/utils.py:1127: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
-      "\u001b[32m2025-03-31 04:01:08.747\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m123\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:08.839\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m163\u001b[0m - \u001b[1mONNX model validation successful\u001b[0m\n",
-      "\u001b[32m2025-03-31 04:01:11.256\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m180\u001b[0m - \u001b[1mONNX model simplification successful\u001b[0m\n"
+      "\u001b[32m2025-03-31 05:47:56.884\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m180\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:56.915\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m271\u001b[0m - \u001b[1mSimplifying ONNX model: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx -> /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:59.406\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m281\u001b[0m - \u001b[32m\u001b[1mONNX model simplification successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 05:47:59.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m239\u001b[0m - \u001b[1mONNX model validation successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
      ]
     }
    ],
@@ -58,6 +61,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/deimkit/exporter.py b/src/deimkit/exporter.py
index adbfebf7..26c4f1cb 100644
--- a/src/deimkit/exporter.py
+++ b/src/deimkit/exporter.py
@@ -2,7 +2,7 @@
 import torch
 import torch.nn as nn
 from loguru import logger
-from typing import Any, Dict
+from typing import Any, Dict, Optional, Tuple
 
 
 class Exporter:
@@ -26,10 +26,14 @@ def to_onnx(
         self,
         checkpoint_path: str,
         output_path: str | None = None,
-        input_shape: tuple[int, int, int, int] | None = None,
+        input_shape: Optional[Tuple[int, int, int, int]] = None,
         check: bool = True,
         simplify: bool = True,
-    ) -> None:
+        dynamic_batch: bool = True,
+        fp16: bool = False,
+        opset_version: int = 20,
+        device: Optional[str] = None,
+    ) -> str:
         """
         Export a model to ONNX format from a checkpoint file.
 
@@ -40,13 +44,32 @@ def to_onnx(
                          If None, will be determined from the config.
             check: Whether to validate the exported ONNX model
             simplify: Whether to simplify the exported ONNX model
+            dynamic_batch: Whether to allow dynamic batch size in the exported model.
+            fp16: Whether to export the model in FP16 precision (requires CUDA).
+            opset_version: The ONNX opset version to use for export.
+            device: The device to use for export ('cpu' or 'cuda'). Auto-selected if None.
 
         Returns:
             Path to the exported ONNX model
         """
+        # Determine device
+        _device_str = device if device else ("cuda" if fp16 else "cpu")
+        if fp16 and _device_str == "cpu":
+            logger.warning("FP16 export requested but device is CPU. Switching to CUDA.")
+            _device_str = "cuda"
+        if _device_str == "cuda" and not torch.cuda.is_available():
+            logger.warning("CUDA device requested but not available. Switching to CPU.")
+            _device_str = "cpu"
+            if fp16:
+                logger.warning("FP16 export disabled as CUDA is not available.")
+                fp16 = False
+
+        _device = torch.device(_device_str)
+        logger.info(f"Using device: {_device}")
+
         # Load checkpoint
         logger.info(f"Loading checkpoint from {checkpoint_path}")
-        checkpoint = torch.load(checkpoint_path, map_location="cpu")
+        checkpoint = torch.load(checkpoint_path, map_location=_device)
 
         # Extract state dict
         if "ema" in checkpoint and "module" in checkpoint["ema"]:
@@ -54,7 +77,10 @@ def to_onnx(
             state_dict = checkpoint["ema"]["module"]
         else:
             logger.info("EMA weights not found, using regular model weights")
-            state_dict = checkpoint.get("model")
+            state_dict = checkpoint.get("model", checkpoint.get("state_dict"))
+        if state_dict is None:
+            logger.error("Could not find model state_dict in checkpoint.")
+            raise KeyError("Checkpoint does not contain 'model' or 'state_dict' key.")
 
         # Load state dict into model
         self.config.model.load_state_dict(state_dict)
@@ -65,22 +91,33 @@ def to_onnx(
 
         # Determine output path if not provided
         if output_path is None:
-            output_path = checkpoint_path.replace(".pth", ".onnx")
+            base_name = os.path.splitext(os.path.basename(checkpoint_path))[0]
+            suffix = ""
+            if dynamic_batch:
+                suffix += "_n_batch"
+            if fp16:
+                suffix += "_fp16"
+            output_path = f"{base_name}{suffix}.onnx"
+        else:
+            os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
 
-        # Create wrapper model
-        wrapper_model = self._create_wrapper_model(model, postprocessor)
+        # Create wrapper model and move to device
+        wrapper_model = self._create_wrapper_model(model, postprocessor).to(_device)
+        wrapper_model.eval()
 
         # Determine input shape from config if not provided
         if input_shape is None:
             logger.info("Input shape not provided, getting size from config")
-            # Get base size from config
             base_size = self.config.get(
                 "yaml_cfg.train_dataloader.collate_fn.base_size", None
+            ) or self.config.get(
+                 "yaml_cfg.val_dataloader.collate_fn.base_size", None
             )
 
             if base_size is None:
                 logger.warning(
-                    "Base size not found in config. Please specify input_shape explicitly."
+                    "Base size not found in config (checked train/val dataloader.collate_fn.base_size)."
+                    " Please specify input_shape explicitly."
                 )
                 raise ValueError(
                     "Could not determine input shape from config. Please provide input_shape parameter."
@@ -88,52 +125,88 @@ def to_onnx(
 
             if isinstance(base_size, (list, tuple)) and len(base_size) == 2:
                 height, width = base_size
-            else:
+            elif isinstance(base_size, int):
                 height, width = base_size, base_size
+            else:
+                 logger.error(f"Unexpected base_size format in config: {base_size}")
+                 raise ValueError("Invalid base_size format in config.")
 
-            # Default to 3 channels (RGB) and batch size of 1
             input_shape = (1, 3, height, width)
             logger.info(f"Using input shape from config: {input_shape}")
 
-        # Create dummy inputs
-        dummy_data = torch.rand(*input_shape)
-        dummy_size = torch.tensor([[input_shape[2], input_shape[3]]])
+        # Create dummy inputs and move to device
+        dummy_data = torch.rand(*input_shape, device=_device)
+        dummy_size = torch.tensor([[input_shape[2], input_shape[3]]], device=_device)
+        if not dynamic_batch and input_shape[0] > 1:
+             dummy_size = dummy_size.repeat(input_shape[0], 1)
 
         # Ensure output directory exists
         os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
 
         # Define dynamic axes
-        dynamic_axes = {"images": {0: "N"}, "orig_target_sizes": {0: "N"}}
+        _dynamic_axes = None
+        if dynamic_batch:
+            _dynamic_axes = {
+                "images": {0: "N"},
+                "orig_target_sizes": {0: "N"},
+                "labels": {0: "N"},
+                "boxes": {0: "N"},
+                "scores": {0: "N"},
+            }
+        logger.info(f"Using dynamic axes: {_dynamic_axes}")
 
         logger.info(f"Exporting model to ONNX: {output_path}")
 
         try:
-            # Export to ONNX
-            torch.onnx.export(
-                wrapper_model,
-                (dummy_data, dummy_size),
-                output_path,
-                input_names=["images", "orig_target_sizes"],
-                output_names=["labels", "boxes", "scores"],
-                dynamic_axes=dynamic_axes,
-                opset_version=20,
-                do_constant_folding=True,
-            )
+            # Export to ONNX with FP16 context if enabled
+            export_kwargs = {
+                "model": wrapper_model,
+                "args": (dummy_data, dummy_size),
+                "f": output_path,
+                "input_names": ["images", "orig_target_sizes"],
+                "output_names": ["labels", "boxes", "scores"],
+                "dynamic_axes": _dynamic_axes,
+                "opset_version": opset_version,
+                "do_constant_folding": True,
+            }
+
+            if fp16:
+                with torch.autocast(device_type=_device_str, dtype=torch.float16):
+                    _ = wrapper_model(dummy_data, dummy_size)
+                    torch.onnx.export(**export_kwargs)
+            else:
+                torch.onnx.export(**export_kwargs)
 
-            logger.success("ONNX export completed successfully")
+            logger.success(f"ONNX export completed successfully: {output_path}")
 
             # Validate and simplify if requested
-            if check:
-                self._check_onnx_model(output_path)
+            final_output_path = output_path
 
             if simplify:
-                self._simplify_onnx_model(
+                simplified_path = self._simplify_onnx_model(
                     output_path,
-                    {"images": dummy_data.shape, "orig_target_sizes": dummy_size.shape},
+                    input_shapes={
+                        "images": dummy_data.shape,
+                        "orig_target_sizes": dummy_size.shape,
+                    },
+                    target_path=output_path,
                 )
+                if simplified_path:
+                     final_output_path = simplified_path
+
+            if check:
+                self._check_onnx_model(final_output_path)
+
+            return final_output_path
 
         except Exception as e:
-            logger.error(f"ONNX export failed: {str(e)}")
+            logger.error(f"ONNX export failed: {str(e)}", exc_info=True)
+            if os.path.exists(output_path):
+                try:
+                    os.remove(output_path)
+                    logger.info(f"Removed partially exported file: {output_path}")
+                except OSError as remove_err:
+                    logger.warning(f"Failed to remove partial file {output_path}: {remove_err}")
             raise RuntimeError(f"Failed to export model to ONNX: {str(e)}") from e
 
     def _create_wrapper_model(
@@ -155,30 +228,81 @@ def forward(self, images: torch.Tensor, orig_target_sizes: torch.Tensor):
 
     def _check_onnx_model(self, model_path: str) -> None:
         """Check if the exported ONNX model is valid."""
+        if not os.path.exists(model_path):
+             logger.error(f"Cannot check ONNX model: File not found at {model_path}")
+             return
         try:
             import onnx
 
             onnx_model = onnx.load(model_path)
             onnx.checker.check_model(onnx_model)
-            logger.info("ONNX model validation successful")
+            logger.info(f"ONNX model validation successful: {model_path}")
         except ImportError:
-            logger.warning("ONNX validation skipped: onnx package not installed")
+            logger.warning("ONNX validation skipped: 'onnx' package not installed")
+        except Exception as e:
+            logger.error(f"ONNX model validation failed for {model_path}: {str(e)}", exc_info=True)
 
     def _simplify_onnx_model(
-        self, model_path: str, input_shapes: dict[str, tuple]
-    ) -> None:
-        """Simplify the exported ONNX model."""
+        self,
+        model_path: str,
+        input_shapes: dict[str, tuple],
+        target_path: Optional[str] = None,
+    ) -> Optional[str]:
+        """
+        Simplify the exported ONNX model using onnxsim.
+
+        Args:
+            model_path: Path to the input ONNX model.
+            input_shapes: Dictionary mapping input names to their shapes for simplification.
+            target_path: Path to save the simplified model. If None, saves inplace (overwrites model_path).
+
+        Returns:
+            Path to the simplified model, or None if simplification failed.
+        """
+        if not os.path.exists(model_path):
+             logger.error(f"Cannot simplify ONNX model: File not found at {model_path}")
+             return None
+        if target_path is None:
+            target_path = model_path
         try:
             import onnx
             import onnxsim
 
+            logger.info(f"Simplifying ONNX model: {model_path} -> {target_path}")
             onnx_model_simplify, check = onnxsim.simplify(
-                model_path, test_input_shapes=input_shapes
+                model_path,
+                test_input_shapes=input_shapes,
+                perform_optimization=True,
+                skip_fuse_bn=False,
             )
-            onnx.save(onnx_model_simplify, model_path)
-            status = "successful" if check else "failed"
-            logger.info(f"ONNX model simplification {status}")
+
+            if check:
+                onnx.save(onnx_model_simplify, target_path)
+                logger.success(f"ONNX model simplification successful: {target_path}")
+                return target_path
+            else:
+                logger.error(f"ONNX model simplification check failed for: {model_path}")
+                if model_path != target_path and os.path.exists(model_path):
+                     import shutil
+                     shutil.copyfile(model_path, target_path)
+                     logger.warning(f"Saved original (unsimplified) model to {target_path} due to check failure.")
+                     return target_path
+                return None
+
         except ImportError:
             logger.warning(
-                "ONNX simplification skipped: required packages not installed"
+                "ONNX simplification skipped: 'onnx' or 'onnxsim' package not installed"
             )
+            return None
+        except Exception as e:
+            logger.error(f"ONNX model simplification failed for {model_path}: {str(e)}", exc_info=True)
+            if model_path != target_path and os.path.exists(model_path):
+                 import shutil
+                 try:
+                      shutil.copyfile(model_path, target_path)
+                      logger.warning(f"Saved original model to {target_path} due to simplification error.")
+                      return target_path
+                 except Exception as copy_e:
+                      logger.error(f"Failed to copy original model {model_path} to {target_path}: {copy_e}")
+
+            return None

From 1dd1191347909638358941552e2bae8eec7291a0 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 12:00:03 +0800
Subject: [PATCH 03/41] add sample inference for image

---
 scripts/image_inference.py | 159 +++++++++++++++++++++++++++++++++++++
 1 file changed, 159 insertions(+)
 create mode 100644 scripts/image_inference.py

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
new file mode 100644
index 00000000..9fd6bead
--- /dev/null
+++ b/scripts/image_inference.py
@@ -0,0 +1,159 @@
+import numpy as np
+import onnxruntime as ort
+import colorsys
+import cv2
+
+def generate_colors(num_classes):
+    """Generate distinct colors for visualization."""
+    hsv_tuples = [(x / num_classes, 0.8, 0.9) for x in range(num_classes)]
+    colors = []
+    for hsv in hsv_tuples:
+        rgb = colorsys.hsv_to_rgb(*hsv)
+        colors.append(tuple(int(255 * x) for x in rgb))
+    return colors
+
+def draw_boxes(image, labels, boxes, scores, ratio, padding, threshold=0.3, class_names=None):
+    """Draw bounding boxes on the image."""
+    # Generate colors for classes
+    num_classes = len(class_names) if class_names else 91
+    colors = generate_colors(num_classes)
+    
+    # Filter detections by threshold
+    valid_indices = scores > threshold
+    labels = labels[valid_indices]
+    boxes = boxes[valid_indices]
+    scores = scores[valid_indices]
+    
+    pad_w, pad_h = padding
+    
+    for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
+        # Get color for this class
+        class_idx = int(lbl)
+        color = colors[class_idx % len(colors)]
+        
+        # Adjust bounding box coordinates
+        box_coords = [
+            int((box[0] - pad_w) / ratio),
+            int((box[1] - pad_h) / ratio),
+            int((box[2] - pad_w) / ratio),
+            int((box[3] - pad_h) / ratio),
+        ]
+        
+        # Draw rectangle
+        cv2.rectangle(image, (box_coords[0], box_coords[1]), (box_coords[2], box_coords[3]), color, 2)
+        
+        # Prepare label text
+        if class_names and class_idx < len(class_names):
+            label_text = f"{class_names[class_idx]} {score:.2f}"
+        else:
+            label_text = f"Class {class_idx} {score:.2f}"
+        
+        # Get text size
+        (text_width, text_height), _ = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
+        
+        # Draw text background
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1] - text_height - 4),
+            (box_coords[0] + text_width + 4, box_coords[1]),
+            color,
+            -1  # Filled rectangle
+        )
+        
+        # Calculate text color based on background brightness
+        brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
+        text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
+        
+        # Draw text
+        cv2.putText(
+            image,
+            label_text,
+            (box_coords[0] + 2, box_coords[1] - 2),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.7,
+            text_color,
+            2
+        )
+    
+    return image
+
+def run_inference(model_path, image_path, class_names_path=None, input_size=640):
+    """Run object detection inference on an image using ONNX model."""
+    # Load ONNX model
+    print(f"Loading ONNX model from {model_path}...")
+    session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+    print(f"Using provider: {session.get_providers()[0]}")
+    
+    # Load class names if provided
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            print(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            print(f"Error loading class names: {e}")
+    
+    # Load image
+    image = cv2.imread(image_path)  # Load as BGR
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+    original_image = image.copy()
+    
+    # Prepare input tensor - just transpose, no normalization needed
+    im_data = np.ascontiguousarray(
+        image.transpose(2, 0, 1),  # HWC to CHW format
+        dtype=np.float32,
+    )
+    im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
+    orig_size = np.array([[image.shape[0], image.shape[1]]], dtype=np.int64)
+    
+    # Get input name from model metadata
+    input_name = session.get_inputs()[0].name
+    
+    # Run inference
+    outputs = session.run(
+        output_names=None,
+        input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+    )
+    
+    # Process outputs
+    labels, boxes, scores = outputs
+
+    # print(outputs)
+    
+    # Draw bounding boxes on the image
+    result_image = draw_boxes(
+        original_image, 
+        labels[0], 
+        boxes[0], 
+        scores[0],
+        1.0,  # No ratio needed since we're not resizing
+        (0, 0),  # No padding needed
+        class_names=class_names
+    )
+    
+    # Save and show result
+    output_path = "detection_result.jpg"
+    result_bgr = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)  # Convert back to BGR for OpenCV
+    cv2.imwrite(output_path, result_bgr)
+    print(f"Detection complete. Result saved to {output_path}")
+    
+    # Display the result
+    cv2.imshow("Detection Result", result_bgr)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    
+    return result_image
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Simple ONNX object detection")
+    parser.add_argument("--model", type=str, required=True, help="Path to ONNX model file")
+    parser.add_argument("--image", type=str, required=True, help="Path to input image")
+    parser.add_argument("--classes", type=str, help="Path to class names file (optional)")
+    parser.add_argument("--size", type=int, default=640, help="Input size for model (default: 640)")
+    
+    args = parser.parse_args()
+    
+    run_inference(args.model, args.image, args.classes, args.size)
\ No newline at end of file

From 03819c283d3cfbfe723f9d275909c63a447a2930 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 12:42:05 +0800
Subject: [PATCH 04/41] working inference

---
 detection_result.jpg       | Bin 0 -> 63015 bytes
 nbs/export.ipynb           |  37 ++++---
 scripts/image_inference.py | 105 ++++++++++--------
 scripts/live_inference.py  |  69 +++++++-----
 scripts/train.py           |   2 +-
 src/deimkit/exporter.py    | 214 ++++++++++++++++++++++++++++++-------
 6 files changed, 301 insertions(+), 126 deletions(-)
 create mode 100644 detection_result.jpg

diff --git a/detection_result.jpg b/detection_result.jpg
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diff --git a/nbs/export.ipynb b/nbs/export.ipynb
index 14d29627..e3c7ef99 100644
--- a/nbs/export.ipynb
+++ b/nbs/export.ipynb
@@ -9,13 +9,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-31 05:47:54.978\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mUsing device: cpu\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:54.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m71\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:55.301\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m76\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:55.436\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:55.437\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mUsing input shape from config: (1, 3, 640, 640)\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:55.443\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m156\u001b[0m - \u001b[1mUsing dynamic axes: {'images': {0: 'N'}, 'orig_target_sizes': {0: 'N'}, 'labels': {0: 'N'}, 'boxes': {0: 'N'}, 'scores': {0: 'N'}}\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:55.443\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m158\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
+      "\u001b[32m2025-03-31 10:35:25.954\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mUsing device: cpu\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:25.955\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m126\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best.pth\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.278\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m131\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m162\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1mUsing target shape from config: (1, 3, 640, 640)\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mInitialized PreprocessingModule with target size: (640, 640)\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m201\u001b[0m - \u001b[1mIncluding preprocessing steps in the ONNX model.\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.474\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m260\u001b[0m - \u001b[1mUsing input names: ['input_bgr', 'orig_target_sizes']\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m261\u001b[0m - \u001b[1mUsing output names: ['labels', 'boxes', 'scores']\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m262\u001b[0m - \u001b[1mUsing dynamic axes: {'input_bgr': {0: 'N', 2: 'H', 3: 'W'}, 'orig_target_sizes': {0: 'N'}, 'labels': {0: 'N'}, 'boxes': {0: 'N'}, 'scores': {0: 'N'}}\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.476\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m263\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.486\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Resized shape: torch.Size([1, 3, 640, 640])\u001b[0m\n",
+      "/home/dnth/Desktop/DEIMKit/src/deimkit/exporter.py:58: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  if x.shape[1] != 3:\n",
+      "\u001b[32m2025-03-31 10:35:26.491\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Swapped BGR to RGB\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:26.493\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m67\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Normalized pixel values to [0, 1]\u001b[0m\n"
      ]
     },
     {
@@ -41,10 +50,11 @@
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
       "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/utils.py:1127: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
-      "\u001b[32m2025-03-31 05:47:56.884\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m180\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:56.915\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m271\u001b[0m - \u001b[1mSimplifying ONNX model: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx -> /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:59.406\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m281\u001b[0m - \u001b[32m\u001b[1mONNX model simplification successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 05:47:59.478\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m239\u001b[0m - \u001b[1mONNX model validation successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\u001b[0m\n"
+      "\u001b[32m2025-03-31 10:35:28.011\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m285\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:28.011\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m296\u001b[0m - \u001b[1mSimplifying ONNX model with input shapes: {'input_bgr': torch.Size([1, 3, 640, 640]), 'orig_target_sizes': torch.Size([1, 2])}\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:28.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m401\u001b[0m - \u001b[1mSimplifying ONNX model: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx -> /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:30.128\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m411\u001b[0m - \u001b[32m\u001b[1mONNX model simplification successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
+      "\u001b[32m2025-03-31 10:35:30.207\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m368\u001b[0m - \u001b[1mONNX model validation successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n"
      ]
     }
    ],
@@ -56,8 +66,9 @@
     "exporter = Exporter(config)\n",
     "\n",
     "output_path = exporter.to_onnx(\n",
-    "    checkpoint_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.pth\",\n",
-    "    output_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/best.onnx\"\n",
+    "    checkpoint_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best.pth\",\n",
+    "    output_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\",\n",
+    "    include_preprocessing=True\n",
     ")"
    ]
   },
diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 9fd6bead..3d8d2a8d 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -1,7 +1,9 @@
-import numpy as np
-import onnxruntime as ort
 import colorsys
+
 import cv2
+import numpy as np
+import onnxruntime as ort
+
 
 def generate_colors(num_classes):
     """Generate distinct colors for visualization."""
@@ -12,25 +14,28 @@ def generate_colors(num_classes):
         colors.append(tuple(int(255 * x) for x in rgb))
     return colors
 
-def draw_boxes(image, labels, boxes, scores, ratio, padding, threshold=0.3, class_names=None):
+
+def draw_boxes(
+    image, labels, boxes, scores, ratio, padding, threshold=0.3, class_names=None
+):
     """Draw bounding boxes on the image."""
     # Generate colors for classes
     num_classes = len(class_names) if class_names else 91
     colors = generate_colors(num_classes)
-    
+
     # Filter detections by threshold
     valid_indices = scores > threshold
     labels = labels[valid_indices]
     boxes = boxes[valid_indices]
     scores = scores[valid_indices]
-    
+
     pad_w, pad_h = padding
-    
+
     for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
         # Get color for this class
         class_idx = int(lbl)
         color = colors[class_idx % len(colors)]
-        
+
         # Adjust bounding box coordinates
         box_coords = [
             int((box[0] - pad_w) / ratio),
@@ -38,32 +43,40 @@ def draw_boxes(image, labels, boxes, scores, ratio, padding, threshold=0.3, clas
             int((box[2] - pad_w) / ratio),
             int((box[3] - pad_h) / ratio),
         ]
-        
+
         # Draw rectangle
-        cv2.rectangle(image, (box_coords[0], box_coords[1]), (box_coords[2], box_coords[3]), color, 2)
-        
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1]),
+            (box_coords[2], box_coords[3]),
+            color,
+            2,
+        )
+
         # Prepare label text
         if class_names and class_idx < len(class_names):
             label_text = f"{class_names[class_idx]} {score:.2f}"
         else:
             label_text = f"Class {class_idx} {score:.2f}"
-        
+
         # Get text size
-        (text_width, text_height), _ = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
-        
+        (text_width, text_height), _ = cv2.getTextSize(
+            label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
+        )
+
         # Draw text background
         cv2.rectangle(
             image,
             (box_coords[0], box_coords[1] - text_height - 4),
             (box_coords[0] + text_width + 4, box_coords[1]),
             color,
-            -1  # Filled rectangle
+            -1,  # Filled rectangle
         )
-        
+
         # Calculate text color based on background brightness
         brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
         text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
-        
+
         # Draw text
         cv2.putText(
             image,
@@ -72,18 +85,17 @@ def draw_boxes(image, labels, boxes, scores, ratio, padding, threshold=0.3, clas
             cv2.FONT_HERSHEY_SIMPLEX,
             0.7,
             text_color,
-            2
+            2,
         )
-    
+
     return image
 
-def run_inference(model_path, image_path, class_names_path=None, input_size=640):
-    """Run object detection inference on an image using ONNX model."""
-    # Load ONNX model
+
+def run_inference(model_path, image_path, class_names_path=None):
     print(f"Loading ONNX model from {model_path}...")
     session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
     print(f"Using provider: {session.get_providers()[0]}")
-    
+
     # Load class names if provided
     class_names = None
     if class_names_path:
@@ -93,67 +105,72 @@ def run_inference(model_path, image_path, class_names_path=None, input_size=640)
             print(f"Loaded {len(class_names)} class names")
         except Exception as e:
             print(f"Error loading class names: {e}")
-    
+
     # Load image
     image = cv2.imread(image_path)  # Load as BGR
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
     original_image = image.copy()
-    
-    # Prepare input tensor - just transpose, no normalization needed
+
     im_data = np.ascontiguousarray(
         image.transpose(2, 0, 1),  # HWC to CHW format
         dtype=np.float32,
     )
     im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
     orig_size = np.array([[image.shape[0], image.shape[1]]], dtype=np.int64)
-    
+
     # Get input name from model metadata
     input_name = session.get_inputs()[0].name
-    
+
     # Run inference
     outputs = session.run(
         output_names=None,
         input_feed={input_name: im_data, "orig_target_sizes": orig_size},
     )
-    
+
     # Process outputs
     labels, boxes, scores = outputs
 
     # print(outputs)
-    
+
     # Draw bounding boxes on the image
     result_image = draw_boxes(
-        original_image, 
-        labels[0], 
-        boxes[0], 
+        original_image,
+        labels[0],
+        boxes[0],
         scores[0],
         1.0,  # No ratio needed since we're not resizing
         (0, 0),  # No padding needed
-        class_names=class_names
+        class_names=class_names,
     )
-    
+
     # Save and show result
     output_path = "detection_result.jpg"
-    result_bgr = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)  # Convert back to BGR for OpenCV
+    result_bgr = cv2.cvtColor(
+        result_image, cv2.COLOR_RGB2BGR
+    )  # Convert back to BGR for OpenCV
     cv2.imwrite(output_path, result_bgr)
     print(f"Detection complete. Result saved to {output_path}")
-    
+
     # Display the result
     cv2.imshow("Detection Result", result_bgr)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
-    
+
     return result_image
 
+
 if __name__ == "__main__":
     import argparse
-    
+
     parser = argparse.ArgumentParser(description="Simple ONNX object detection")
-    parser.add_argument("--model", type=str, required=True, help="Path to ONNX model file")
+    parser.add_argument(
+        "--model", type=str, required=True, help="Path to ONNX model file"
+    )
     parser.add_argument("--image", type=str, required=True, help="Path to input image")
-    parser.add_argument("--classes", type=str, help="Path to class names file (optional)")
-    parser.add_argument("--size", type=int, default=640, help="Input size for model (default: 640)")
-    
+    parser.add_argument(
+        "--classes", type=str, help="Path to class names file (optional)"
+    )
+
     args = parser.parse_args()
-    
-    run_inference(args.model, args.image, args.classes, args.size)
\ No newline at end of file
+
+    run_inference(args.model, args.image, args.classes)
diff --git a/scripts/live_inference.py b/scripts/live_inference.py
index 929db73b..c9930824 100644
--- a/scripts/live_inference.py
+++ b/scripts/live_inference.py
@@ -38,7 +38,7 @@ def generate_colors(num_classes):
     return colors
 
 
-def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4, class_names=None):
+def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.3, class_names=None):
     result_images = []
 
     # Generate colors for classes
@@ -113,18 +113,23 @@ def process_image(sess, im_pil, class_names=None, input_size=640):
         [[resized_im_pil.size[1], resized_im_pil.size[0]]], dtype=np.int64
     )
 
-    # Convert PIL image to numpy array and normalize to 0-1 range
-    im_data = np.array(resized_im_pil, dtype=np.float32) / 255.0
+    # Get input name from model metadata
+    input_name = sess.get_inputs()[0].name
+    is_preprocessed = input_name == "input_bgr"
 
-    # Transpose from HWC to CHW format (height, width, channels) -> (channels, height, width)
-    im_data = im_data.transpose(2, 0, 1)
-
-    # Add batch dimension
-    im_data = np.expand_dims(im_data, axis=0)
+    if is_preprocessed:
+        # For models with preprocessing, input should be BGR float32 format
+        im_data = cv2.cvtColor(np.array(resized_im_pil), cv2.COLOR_RGB2BGR)
+        im_data = im_data.astype(np.float32)  # Convert to float32
+        im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
+    else:
+        # For models without preprocessing, normalize to 0-1 range
+        im_data = np.array(resized_im_pil, dtype=np.float32) / 255.0
+        im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
 
     output = sess.run(
         output_names=None,
-        input_feed={"images": im_data, "orig_target_sizes": orig_size},
+        input_feed={input_name: im_data, "orig_target_sizes": orig_size},
     )
 
     labels, boxes, scores = output
@@ -178,6 +183,10 @@ def process_video(sess, video_path, class_names=None, input_size=640):
     show_provider = True
     provider = sess.get_providers()[0]  # Get the first active provider
 
+    # Get input name from model metadata
+    input_name = sess.get_inputs()[0].name
+    is_preprocessed = input_name == "input_bgr"
+
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
@@ -200,18 +209,19 @@ def process_video(sess, video_path, class_names=None, input_size=640):
             [[resized_frame_pil.size[1], resized_frame_pil.size[0]]], dtype=np.int64
         )
 
-        # Convert PIL image to numpy array and normalize to 0-1 range
-        im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
-
-        # Transpose from HWC to CHW format (height, width, channels) -> (channels, height, width)
-        im_data = im_data.transpose(2, 0, 1)
-
-        # Add batch dimension
-        im_data = np.expand_dims(im_data, axis=0)
+        if is_preprocessed:
+            # For models with preprocessing, input should be BGR float32 format
+            im_data = cv2.cvtColor(np.array(resized_frame_pil), cv2.COLOR_RGB2BGR)
+            im_data = im_data.astype(np.float32)  # Convert to float32
+            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
+        else:
+            # For models without preprocessing, normalize to 0-1 range
+            im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
+            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
 
         output = sess.run(
             output_names=None,
-            input_feed={"images": im_data, "orig_target_sizes": orig_size},
+            input_feed={input_name: im_data, "orig_target_sizes": orig_size},
         )
 
         labels, boxes, scores = output
@@ -327,6 +337,10 @@ def process_webcam(sess, device_id=0, class_names=None, input_size=640):
     show_provider = True
     provider = sess.get_providers()[0]
 
+    # Get input name from model metadata
+    input_name = sess.get_inputs()[0].name
+    is_preprocessed = input_name == "input_bgr"
+
     while True:
         # Calculate FPS
         curr_time = time.time()
@@ -350,18 +364,19 @@ def process_webcam(sess, device_id=0, class_names=None, input_size=640):
             [[resized_frame_pil.size[1], resized_frame_pil.size[0]]], dtype=np.int64
         )
 
-        # Convert PIL image to numpy array and normalize to 0-1 range
-        im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
-
-        # Transpose from HWC to CHW format (height, width, channels) -> (channels, height, width)
-        im_data = im_data.transpose(2, 0, 1)
-
-        # Add batch dimension
-        im_data = np.expand_dims(im_data, axis=0)
+        if is_preprocessed:
+            # For models with preprocessing, input should be BGR float32 format
+            im_data = cv2.cvtColor(np.array(resized_frame_pil), cv2.COLOR_RGB2BGR)
+            im_data = im_data.astype(np.float32)  # Convert to float32
+            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
+        else:
+            # For models without preprocessing, normalize to 0-1 range
+            im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
+            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
 
         output = sess.run(
             output_names=None,
-            input_feed={"images": im_data, "orig_target_sizes": orig_size},
+            input_feed={input_name: im_data, "orig_target_sizes": orig_size},
         )
 
         labels, boxes, scores = output
diff --git a/scripts/train.py b/scripts/train.py
index 83bd6935..ace183b9 100644
--- a/scripts/train.py
+++ b/scripts/train.py
@@ -15,7 +15,7 @@
     val_batch_size=20,
     num_classes=4,
     remap_mscoco=False,
-    output_dir="./outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100",
+    output_dir="./outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_pinto",
 )
 
 trainer = Trainer(conf)
diff --git a/src/deimkit/exporter.py b/src/deimkit/exporter.py
index 26c4f1cb..f4b27ef0 100644
--- a/src/deimkit/exporter.py
+++ b/src/deimkit/exporter.py
@@ -1,6 +1,7 @@
 import os
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from loguru import logger
 from typing import Any, Dict, Optional, Tuple
 
@@ -10,7 +11,7 @@ class Exporter:
     Export a DEIM model to ONNX format.
 
     This class provides functionality to export trained DEIM models to ONNX format
-    for deployment in production environments.
+    for deployment in production environments, optionally including preprocessing steps.
     """
 
     def __init__(self, config: Any):
@@ -22,6 +23,51 @@ def __init__(self, config: Any):
         """
         self.config = config
 
+    class PreprocessingModule(nn.Module):
+        """Handles image preprocessing: resize, BGR->RGB, normalize."""
+        def __init__(self, target_height: int, target_width: int):
+            super().__init__()
+            self.target_height = target_height
+            self.target_width = target_width
+            logger.info(
+                f"Initialized PreprocessingModule with target size: "
+                f"({target_height}, {target_width})"
+            )
+
+        def forward(self, input_bgr: torch.Tensor) -> torch.Tensor:
+            """
+            Apply preprocessing steps.
+
+            Args:
+                input_bgr: Input tensor in BGR format (N, 3, H, W).
+
+            Returns:
+                Preprocessed tensor in RGB format, normalized, and resized.
+            """
+            # 1. Resize
+            x = F.interpolate(
+                input=input_bgr,
+                size=(self.target_height, self.target_width),
+                mode='bilinear', # Common interpolation mode, adjust if needed
+                align_corners=False # Common practice
+            )
+            logger.debug(f"Preprocessing: Resized shape: {x.shape}")
+
+            # 2. BGR -> RGB
+            # Ensure input has 3 channels
+            if x.shape[1] != 3:
+                 raise ValueError(f"Input tensor must have 3 channels (BGR), got {x.shape[1]}")
+            # Swap channels: (B:0, G:1, R:2) -> (R:2, G:1, B:0)
+            x = x[:, [2, 1, 0], :, :]
+            logger.debug("Preprocessing: Swapped BGR to RGB")
+
+            # 3. Normalize (0-255 -> 0-1)
+            # Assuming input is uint8 [0, 255], scale to [0, 1]
+            x = x * (1.0 / 255.0)
+            logger.debug("Preprocessing: Normalized pixel values to [0, 1]")
+
+            return x
+
     def to_onnx(
         self,
         checkpoint_path: str,
@@ -30,6 +76,8 @@ def to_onnx(
         check: bool = True,
         simplify: bool = True,
         dynamic_batch: bool = True,
+        dynamic_input_size: bool = True,
+        include_preprocessing: bool = False,
         fp16: bool = False,
         opset_version: int = 20,
         device: Optional[str] = None,
@@ -41,10 +89,17 @@ def to_onnx(
             checkpoint_path: Path to the checkpoint file (.pth)
             output_path: Path for the ONNX model (defaults to checkpoint_path with .onnx extension)
             input_shape: Shape of the input tensor (batch_size, channels, height, width).
+                         If `include_preprocessing` is True, this defines the *target* size
+                         for the internal resize operation. Otherwise, it's the direct model input size.
                          If None, will be determined from the config.
             check: Whether to validate the exported ONNX model
             simplify: Whether to simplify the exported ONNX model
-            dynamic_batch: Whether to allow dynamic batch size in the exported model.
+            dynamic_batch: Whether to allow dynamic batch size (N) in the exported model.
+            dynamic_input_size: Whether to allow dynamic input height (H) and width (W)
+                                if `include_preprocessing` is True.
+            include_preprocessing: If True, include resize, BGR->RGB, and normalization
+                                   steps in the exported ONNX graph. The input will
+                                   expect raw BGR images.
             fp16: Whether to export the model in FP16 precision (requires CUDA).
             opset_version: The ONNX opset version to use for export.
             device: The device to use for export ('cpu' or 'cuda'). Auto-selected if None.
@@ -101,11 +156,8 @@ def to_onnx(
         else:
             os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
 
-        # Create wrapper model and move to device
-        wrapper_model = self._create_wrapper_model(model, postprocessor).to(_device)
-        wrapper_model.eval()
-
         # Determine input shape from config if not provided
+        target_height, target_width = None, None
         if input_shape is None:
             logger.info("Input shape not provided, getting size from config")
             base_size = self.config.get(
@@ -131,30 +183,83 @@ def to_onnx(
                  logger.error(f"Unexpected base_size format in config: {base_size}")
                  raise ValueError("Invalid base_size format in config.")
 
-            input_shape = (1, 3, height, width)
-            logger.info(f"Using input shape from config: {input_shape}")
+            target_height, target_width = height, width
+            input_shape = (1, 3, target_height, target_width)
+            logger.info(f"Using target shape from config: {input_shape}")
+        else:
+             # If input_shape is provided, extract target height/width
+             _, _, target_height, target_width = input_shape
+             logger.info(f"Using provided target shape: {(target_height, target_width)}")
+
+        # Create preprocessing module if requested
+        preprocessing_module = None
+        if include_preprocessing:
+             if target_height is None or target_width is None:
+                 raise ValueError("Cannot include preprocessing without a defined target height/width."
+                                  "Provide input_shape or ensure base_size is in config.")
+             preprocessing_module = self.PreprocessingModule(target_height, target_width)
+             logger.info("Including preprocessing steps in the ONNX model.")
 
-        # Create dummy inputs and move to device
-        dummy_data = torch.rand(*input_shape, device=_device)
-        dummy_size = torch.tensor([[input_shape[2], input_shape[3]]], device=_device)
-        if not dynamic_batch and input_shape[0] > 1:
-             dummy_size = dummy_size.repeat(input_shape[0], 1)
+        # Create wrapper model and move to device
+        wrapper_model = self._create_wrapper_model(
+            model, postprocessor, preprocessing_module
+        ).to(_device)
+        wrapper_model.eval()
 
-        # Ensure output directory exists
-        os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
+        # Create dummy inputs and move to device
+        dummy_data_shape = input_shape
+        dummy_data = torch.rand(*dummy_data_shape, device=_device)
+
+        # The 'orig_target_sizes' input typically corresponds to the size *before* padding/resizing
+        # If preprocessing is included, this might represent the original image size.
+        # If not, it represents the size the postprocessor needs.
+        # For export, we use the target H/W here. The actual value depends on runtime usage.
+        dummy_size_h, dummy_size_w = target_height, target_width
+        # If include_preprocessing and dynamic input size, the actual input H/W can vary.
+        # For export dummy data, we still need concrete values.
+        dummy_size = torch.tensor([[dummy_size_h, dummy_size_w]], device=_device)
+        if not dynamic_batch and dummy_data_shape[0] > 1:
+             dummy_size = dummy_size.repeat(dummy_data_shape[0], 1)
+
+        # Define input/output names based on whether preprocessing is included
+        input_names = ["images", "orig_target_sizes"]
+        if include_preprocessing:
+            input_names[0] = "input_bgr"
+
+        output_names = ["labels", "boxes", "scores"]
 
         # Define dynamic axes
-        _dynamic_axes = None
+        _dynamic_axes = {}
+        first_input_name = input_names[0]
+        _dynamic_axes[first_input_name] = {}
+        _dynamic_axes["orig_target_sizes"] = {}
+        _dynamic_axes["labels"] = {}
+        _dynamic_axes["boxes"] = {}
+        _dynamic_axes["scores"] = {}
+
         if dynamic_batch:
-            _dynamic_axes = {
-                "images": {0: "N"},
-                "orig_target_sizes": {0: "N"},
-                "labels": {0: "N"},
-                "boxes": {0: "N"},
-                "scores": {0: "N"},
-            }
+            _dynamic_axes[first_input_name][0] = "N"
+            _dynamic_axes["orig_target_sizes"][0] = "N"
+            _dynamic_axes["labels"][0] = "N"
+            _dynamic_axes["boxes"][0] = "N"
+            _dynamic_axes["scores"][0] = "N"
+
+        # Add dynamic H/W for the input if preprocessing is enabled and requested
+        if include_preprocessing and dynamic_input_size:
+            _dynamic_axes[first_input_name][2] = "H"
+            _dynamic_axes[first_input_name][3] = "W"
+            # Note: 'orig_target_sizes' might also need dynamic axes depending on usage,
+            # but typically it relates to the *original* size before preprocessing.
+            # Keeping it simple here unless specific needs arise.
+
+        # Remove empty dicts if no dynamic axes are specified for a name
+        _dynamic_axes = {k: v for k, v in _dynamic_axes.items() if v}
+        if not _dynamic_axes:
+             _dynamic_axes = None
+
+        logger.info(f"Using input names: {input_names}")
+        logger.info(f"Using output names: {output_names}")
         logger.info(f"Using dynamic axes: {_dynamic_axes}")
-
         logger.info(f"Exporting model to ONNX: {output_path}")
 
         try:
@@ -163,8 +268,8 @@ def to_onnx(
                 "model": wrapper_model,
                 "args": (dummy_data, dummy_size),
                 "f": output_path,
-                "input_names": ["images", "orig_target_sizes"],
-                "output_names": ["labels", "boxes", "scores"],
+                "input_names": input_names,
+                "output_names": output_names,
                 "dynamic_axes": _dynamic_axes,
                 "opset_version": opset_version,
                 "do_constant_folding": True,
@@ -183,12 +288,15 @@ def to_onnx(
             final_output_path = output_path
 
             if simplify:
+                # Input shapes for simplification should match the dummy data used for export
+                input_shapes_for_sim = {
+                    input_names[0]: dummy_data.shape,
+                    input_names[1]: dummy_size.shape,
+                }
+                logger.info(f"Simplifying ONNX model with input shapes: {input_shapes_for_sim}")
                 simplified_path = self._simplify_onnx_model(
                     output_path,
-                    input_shapes={
-                        "images": dummy_data.shape,
-                        "orig_target_sizes": dummy_size.shape,
-                    },
+                    input_shapes=input_shapes_for_sim,
                     target_path=output_path,
                 )
                 if simplified_path:
@@ -210,21 +318,42 @@ def to_onnx(
             raise RuntimeError(f"Failed to export model to ONNX: {str(e)}") from e
 
     def _create_wrapper_model(
-        self, model: nn.Module, postprocessor: nn.Module
+        self,
+        model: nn.Module,
+        postprocessor: nn.Module,
+        preprocessing: Optional[nn.Module] = None
     ) -> nn.Module:
-        """Create a wrapper model that includes both model and postprocessor."""
+        """
+        Create a wrapper model that includes optional preprocessing, the main model,
+        and the postprocessor.
+        """
 
         class WrappedModel(nn.Module):
-            def __init__(self, model: nn.Module, postprocessor: nn.Module):
+            def __init__(
+                self,
+                model: nn.Module,
+                postprocessor: nn.Module,
+                preprocessing: Optional[nn.Module] = None
+            ):
                 super().__init__()
+                self.preprocessing = preprocessing
                 self.model = model
                 self.postprocessor = postprocessor
 
             def forward(self, images: torch.Tensor, orig_target_sizes: torch.Tensor):
-                outputs = self.model(images)
+                # Apply preprocessing if it exists
+                if self.preprocessing:
+                    x = self.preprocessing(images)
+                else:
+                    x = images
+
+                # Pass preprocessed data to the main model
+                outputs = self.model(x)
+
+                # Pass model outputs and original sizes to postprocessor
                 return self.postprocessor(outputs, orig_target_sizes)
 
-        return WrappedModel(model, postprocessor)
+        return WrappedModel(model, postprocessor, preprocessing)
 
     def _check_onnx_model(self, model_path: str) -> None:
         """Check if the exported ONNX model is valid."""
@@ -253,8 +382,9 @@ def _simplify_onnx_model(
 
         Args:
             model_path: Path to the input ONNX model.
-            input_shapes: Dictionary mapping input names to their shapes for simplification.
-            target_path: Path to save the simplified model. If None, saves inplace (overwrites model_path).
+            input_shapes: Dictionary mapping input names (e.g., 'input_bgr', 'orig_target_sizes')
+                          to their concrete shapes for simplification.
+            target_path: Path to save the simplified model. If None, saves inplace.
 
         Returns:
             Path to the simplified model, or None if simplification failed.
@@ -284,9 +414,12 @@ def _simplify_onnx_model(
                 logger.error(f"ONNX model simplification check failed for: {model_path}")
                 if model_path != target_path and os.path.exists(model_path):
                      import shutil
-                     shutil.copyfile(model_path, target_path)
-                     logger.warning(f"Saved original (unsimplified) model to {target_path} due to check failure.")
-                     return target_path
+                     try:
+                         shutil.copyfile(model_path, target_path)
+                         logger.warning(f"Saved original (unsimplified) model to {target_path} due to check failure.")
+                         return target_path
+                     except Exception as copy_e:
+                         logger.error(f"Failed to copy original model {model_path} to {target_path}: {copy_e}")
                 return None
 
         except ImportError:
@@ -304,5 +437,4 @@ def _simplify_onnx_model(
                       return target_path
                  except Exception as copy_e:
                       logger.error(f"Failed to copy original model {model_path} to {target_path}: {copy_e}")
-
             return None

From 46496a71db013806f6a7b8cc4385aefcad8a309f Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 13:20:29 +0800
Subject: [PATCH 05/41] working video det

---
 detection_result.jpg       | Bin 63015 -> 60530 bytes
 scripts/image_inference.py | 117 ++++++++++++++++++++++++++++++++++++-
 2 files changed, 115 insertions(+), 2 deletions(-)

diff --git a/detection_result.jpg b/detection_result.jpg
index e45e491f55493eab71acdee11363134241353c5b..00076cc022852b22a7a808aef57519dcaab55b68 100644
GIT binary patch
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diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 3d8d2a8d..916d2541 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -118,6 +118,9 @@ def run_inference(model_path, image_path, class_names_path=None):
     im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
     orig_size = np.array([[image.shape[0], image.shape[1]]], dtype=np.int64)
 
+    print(f"Image frame shape: {image.shape}")
+    print(f"Processed input shape: {im_data.shape}")
+
     # Get input name from model metadata
     input_name = session.get_inputs()[0].name
 
@@ -159,6 +162,104 @@ def run_inference(model_path, image_path, class_names_path=None):
     return result_image
 
 
+def run_inference_webcam(model_path, class_names_path=None):
+    """Run real-time object detection on webcam feed."""
+    print(f"Loading ONNX model from {model_path}...")
+    session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+    print(f"Using provider: {session.get_providers()[0]}")
+
+    # Load class names if provided
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            print(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            print(f"Error loading class names: {e}")
+
+    # Initialize webcam
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        raise RuntimeError("Failed to open webcam")
+
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                print("Failed to grab frame")
+                break
+
+            # Calculate padding to make the frame square
+            height, width = frame.shape[:2]
+            max_dim = max(height, width)
+            
+            # Create a square black canvas
+            square_frame = np.zeros((max_dim, max_dim, 3), dtype=np.uint8)
+            
+            # Calculate padding
+            pad_top = (max_dim - height) // 2
+            pad_left = (max_dim - width) // 2
+            
+            # Place the original frame in the center of the square canvas
+            square_frame[pad_top:pad_top+height, pad_left:pad_left+width] = frame
+            
+            # Store original dimensions and scale factor
+            scale_factor = 640 / max_dim
+            
+            # Resize to 640x640
+            square_frame = cv2.resize(square_frame, (640, 640))
+
+            # Convert BGR to RGB
+            image = cv2.cvtColor(square_frame, cv2.COLOR_BGR2RGB)
+            original_image = image.copy()
+
+            # Prepare input data
+            im_data = np.ascontiguousarray(
+                image.transpose(2, 0, 1),  # HWC to CHW format
+                dtype=np.float32,
+            )
+            im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
+            orig_size = np.array([[640, 640]], dtype=np.int64)  # Use padded size
+
+            # Get input name from model metadata
+            input_name = session.get_inputs()[0].name
+
+            # Run inference
+            outputs = session.run(
+                output_names=None,
+                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+            )
+
+            # Process outputs
+            labels, boxes, scores = outputs
+
+            # Draw bounding boxes on the image
+            result_image = draw_boxes(
+                original_image,
+                labels[0],
+                boxes[0],
+                scores[0],
+                1.0,  # No ratio needed since we're using the padded size
+                (0, 0),  # No need to adjust for padding here
+                class_names=class_names,
+            )
+
+            # Convert back to BGR for display
+            result_bgr = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
+            
+            # Display the result
+            cv2.imshow("Webcam Detection", result_bgr)
+
+            # Break loop on 'q' press
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+
+
 if __name__ == "__main__":
     import argparse
 
@@ -166,11 +267,23 @@ def run_inference(model_path, image_path, class_names_path=None):
     parser.add_argument(
         "--model", type=str, required=True, help="Path to ONNX model file"
     )
-    parser.add_argument("--image", type=str, required=True, help="Path to input image")
+    parser.add_argument(
+        "--image", type=str, help="Path to input image (optional)"
+    )
+    parser.add_argument(
+        "--webcam", action="store_true", help="Use webcam input"
+    )
     parser.add_argument(
         "--classes", type=str, help="Path to class names file (optional)"
     )
 
     args = parser.parse_args()
 
-    run_inference(args.model, args.image, args.classes)
+    if args.webcam:
+        run_inference_webcam(args.model, args.classes)
+    elif args.image:
+        run_inference(args.model, args.image, args.classes)
+    else:
+        parser.error("Either --image or --webcam must be specified")
+
+    

From 1967b29ae34cc27b620c8e05fc4b75fb63f84704 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 13:34:13 +0800
Subject: [PATCH 06/41] add cuda and trt ep

---
 scripts/image_inference.py | 127 ++++++++++++++++++++++++++++++++++---
 1 file changed, 119 insertions(+), 8 deletions(-)

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 916d2541..f5c51a60 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -1,5 +1,6 @@
 import colorsys
-
+import time
+import torch
 import cv2
 import numpy as np
 import onnxruntime as ort
@@ -162,11 +163,52 @@ def run_inference(model_path, image_path, class_names_path=None):
     return result_image
 
 
-def run_inference_webcam(model_path, class_names_path=None):
+def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
     """Run real-time object detection on webcam feed."""
-    print(f"Loading ONNX model from {model_path}...")
-    session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-    print(f"Using provider: {session.get_providers()[0]}")
+    # Set up providers based on selection
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+
+    try:
+        print(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
+    except Exception as e:
+        print(f"Error creating inference session with providers {providers}: {e}")
+        print("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+
+    # Variables for FPS calculation
+    prev_time = time.time()
+    curr_time = 0
+    fps_display = 0
+    show_provider = True
 
     # Load class names if provided
     class_names = None
@@ -190,6 +232,12 @@ def run_inference_webcam(model_path, class_names_path=None):
                 print("Failed to grab frame")
                 break
 
+            # Calculate FPS
+            curr_time = time.time()
+            if curr_time - prev_time > 0:  # Avoid division by zero
+                fps_display = 1 / (curr_time - prev_time)
+            prev_time = curr_time
+
             # Calculate padding to make the frame square
             height, width = frame.shape[:2]
             max_dim = max(height, width)
@@ -248,12 +296,68 @@ def run_inference_webcam(model_path, class_names_path=None):
             # Convert back to BGR for display
             result_bgr = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
             
+            # Add FPS and provider display
+            fps_text = f"FPS: {fps_display:.1f}"
+            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = result_bgr.shape[1] - text_size[0] - 10
+            text_y = 30
+
+            # Draw FPS background rectangle
+            cv2.rectangle(
+                result_bgr,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw FPS text
+            cv2.putText(
+                result_bgr,
+                fps_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
+
+            # Add provider display
+            if show_provider:
+                provider_text = f"Provider: {session.get_providers()[0]}"
+                text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+                text_x = (result_bgr.shape[1] - text_size[0]) // 2
+                text_y = result_bgr.shape[0] - 20
+
+                # Draw provider background rectangle
+                cv2.rectangle(
+                    result_bgr,
+                    (text_x - 5, text_y - text_size[1] - 5),
+                    (text_x + text_size[0] + 5, text_y + 5),
+                    (139, 0, 0),
+                    -1,
+                )
+
+                # Draw provider text
+                cv2.putText(
+                    result_bgr,
+                    provider_text,
+                    (text_x, text_y),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.8,
+                    (255, 255, 255),
+                    2,
+                )
+
             # Display the result
             cv2.imshow("Webcam Detection", result_bgr)
 
-            # Break loop on 'q' press
-            if cv2.waitKey(1) & 0xFF == ord('q'):
+            # Handle key presses
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord('q'):
                 break
+            elif key == ord('p'):
+                show_provider = not show_provider
 
     finally:
         cap.release()
@@ -276,11 +380,18 @@ def run_inference_webcam(model_path, class_names_path=None):
     parser.add_argument(
         "--classes", type=str, help="Path to class names file (optional)"
     )
+    parser.add_argument(
+        "--provider",
+        type=str,
+        choices=["cpu", "cuda", "tensorrt"],
+        default="cpu",
+        help="ONNXRuntime provider to use for inference",
+    )
 
     args = parser.parse_args()
 
     if args.webcam:
-        run_inference_webcam(args.model, args.classes)
+        run_inference_webcam(args.model, args.classes, args.provider)
     elif args.image:
         run_inference(args.model, args.image, args.classes)
     else:

From ebb41237e31e7c1e208bc6b70df9743fe617e50f Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 13:50:06 +0800
Subject: [PATCH 07/41] working webcam inference

---
 scripts/image_inference.py | 153 ++++++++++++++++++-------------------
 1 file changed, 75 insertions(+), 78 deletions(-)

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index f5c51a60..c4352f51 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -30,19 +30,17 @@ def draw_boxes(
     boxes = boxes[valid_indices]
     scores = scores[valid_indices]
 
-    pad_w, pad_h = padding
-
     for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
         # Get color for this class
         class_idx = int(lbl)
         color = colors[class_idx % len(colors)]
 
-        # Adjust bounding box coordinates
+        # Use box coordinates directly
         box_coords = [
-            int((box[0] - pad_w) / ratio),
-            int((box[1] - pad_h) / ratio),
-            int((box[2] - pad_w) / ratio),
-            int((box[3] - pad_h) / ratio),
+            int(box[0]),  # x1
+            int(box[1]),  # y1
+            int(box[2]),  # x2
+            int(box[3]),  # y2
         ]
 
         # Draw rectangle
@@ -92,7 +90,7 @@ def draw_boxes(
     return image
 
 
-def run_inference(model_path, image_path, class_names_path=None):
+def run_inference(model_path, image_path, class_names_path=None, threshold=0.3):
     print(f"Loading ONNX model from {model_path}...")
     session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
     print(f"Using provider: {session.get_providers()[0]}")
@@ -144,6 +142,7 @@ def run_inference(model_path, image_path, class_names_path=None):
         scores[0],
         1.0,  # No ratio needed since we're not resizing
         (0, 0),  # No padding needed
+        threshold=threshold,
         class_names=class_names,
     )
 
@@ -163,7 +162,7 @@ def run_inference(model_path, image_path, class_names_path=None):
     return result_image
 
 
-def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
+def run_inference_webcam(model_path, class_names_path=None, provider="cpu", threshold=0.3):
     """Run real-time object detection on webcam feed."""
     # Set up providers based on selection
     if provider == "cpu":
@@ -204,12 +203,10 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
         print("Attempting to fall back to CPU execution...")
         session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
 
-    # Variables for FPS calculation
+    # Update FPS calculation variables
     prev_time = time.time()
-    curr_time = 0
     fps_display = 0
-    show_provider = True
-
+    
     # Load class names if provided
     class_names = None
     if class_names_path:
@@ -233,47 +230,38 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
                 break
 
             # Calculate FPS
-            curr_time = time.time()
-            if curr_time - prev_time > 0:  # Avoid division by zero
-                fps_display = 1 / (curr_time - prev_time)
-            prev_time = curr_time
+            current_time = time.time()
+            if current_time - prev_time > 0:  # Avoid division by zero
+                fps_display = 1 / (current_time - prev_time)
+            prev_time = current_time
 
-            # Calculate padding to make the frame square
+            # Calculate scaling and padding
             height, width = frame.shape[:2]
-            max_dim = max(height, width)
-            
-            # Create a square black canvas
-            square_frame = np.zeros((max_dim, max_dim, 3), dtype=np.uint8)
+            scale = 640.0 / max(height, width)
+            new_height = int(height * scale)
+            new_width = int(width * scale)
             
             # Calculate padding
-            pad_top = (max_dim - height) // 2
-            pad_left = (max_dim - width) // 2
+            y_offset = (640 - new_height) // 2
+            x_offset = (640 - new_width) // 2
             
-            # Place the original frame in the center of the square canvas
-            square_frame[pad_top:pad_top+height, pad_left:pad_left+width] = frame
-            
-            # Store original dimensions and scale factor
-            scale_factor = 640 / max_dim
-            
-            # Resize to 640x640
-            square_frame = cv2.resize(square_frame, (640, 640))
-
-            # Convert BGR to RGB
-            image = cv2.cvtColor(square_frame, cv2.COLOR_BGR2RGB)
-            original_image = image.copy()
+            # Create model input with padding
+            model_input = np.zeros((640, 640, 3), dtype=np.uint8)
+            model_input[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = cv2.resize(frame, (new_width, new_height))
 
+            # Convert BGR to RGB for model input
+            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
+            
             # Prepare input data
             im_data = np.ascontiguousarray(
-                image.transpose(2, 0, 1),  # HWC to CHW format
+                image.transpose(2, 0, 1),
                 dtype=np.float32,
             )
-            im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
+            im_data = np.expand_dims(im_data, axis=0)
             orig_size = np.array([[640, 640]], dtype=np.int64)  # Use padded size
 
-            # Get input name from model metadata
+            # Get input name and run inference
             input_name = session.get_inputs()[0].name
-
-            # Run inference
             outputs = session.run(
                 output_names=None,
                 input_feed={input_name: im_data, "orig_target_sizes": orig_size},
@@ -282,20 +270,26 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
             # Process outputs
             labels, boxes, scores = outputs
 
-            # Draw bounding boxes on the image
+            # Scale boxes from padded 640x640 to original frame size
+            boxes = boxes[0]  # Remove batch dimension
+            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
+
+            # Draw bounding boxes on the original frame
             result_image = draw_boxes(
-                original_image,
+                frame,  # Use original frame
                 labels[0],
-                boxes[0],
+                boxes,
                 scores[0],
-                1.0,  # No ratio needed since we're using the padded size
-                (0, 0),  # No need to adjust for padding here
+                1.0,  # No additional scaling needed
+                (0, 0),  # No additional padding needed
+                threshold=threshold,
                 class_names=class_names,
             )
 
-            # Convert back to BGR for display
-            result_bgr = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
-            
+            # No need to convert back to BGR since we're using the original frame
+            result_bgr = result_image
+
             # Add FPS and provider display
             fps_text = f"FPS: {fps_display:.1f}"
             text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
@@ -323,31 +317,30 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
             )
 
             # Add provider display
-            if show_provider:
-                provider_text = f"Provider: {session.get_providers()[0]}"
-                text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-                text_x = (result_bgr.shape[1] - text_size[0]) // 2
-                text_y = result_bgr.shape[0] - 20
-
-                # Draw provider background rectangle
-                cv2.rectangle(
-                    result_bgr,
-                    (text_x - 5, text_y - text_size[1] - 5),
-                    (text_x + text_size[0] + 5, text_y + 5),
-                    (139, 0, 0),
-                    -1,
-                )
-
-                # Draw provider text
-                cv2.putText(
-                    result_bgr,
-                    provider_text,
-                    (text_x, text_y),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    0.8,
-                    (255, 255, 255),
-                    2,
-                )
+            provider_text = f"Provider: {session.get_providers()[0]}"
+            text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = (result_bgr.shape[1] - text_size[0]) // 2
+            text_y = result_bgr.shape[0] - 20
+
+            # Draw provider background rectangle
+            cv2.rectangle(
+                result_bgr,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw provider text
+            cv2.putText(
+                result_bgr,
+                provider_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
 
             # Display the result
             cv2.imshow("Webcam Detection", result_bgr)
@@ -356,8 +349,6 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
             key = cv2.waitKey(1) & 0xFF
             if key == ord('q'):
                 break
-            elif key == ord('p'):
-                show_provider = not show_provider
 
     finally:
         cap.release()
@@ -387,13 +378,19 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu"):
         default="cpu",
         help="ONNXRuntime provider to use for inference",
     )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=0.3,
+        help="Detection confidence threshold (default: 0.3)",
+    )
 
     args = parser.parse_args()
 
     if args.webcam:
-        run_inference_webcam(args.model, args.classes, args.provider)
+        run_inference_webcam(args.model, args.classes, args.provider, args.threshold)
     elif args.image:
-        run_inference(args.model, args.image, args.classes)
+        run_inference(args.model, args.image, args.classes, args.threshold)
     else:
         parser.error("Either --image or --webcam must be specified")
 

From cb9b321dd08d75cc2881fe50d25910e0b409d66e Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 13:56:14 +0800
Subject: [PATCH 08/41] update

---
 scripts/image_inference.py | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index c4352f51..32a3be7a 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -162,7 +162,7 @@ def run_inference(model_path, image_path, class_names_path=None, threshold=0.3):
     return result_image
 
 
-def run_inference_webcam(model_path, class_names_path=None, provider="cpu", threshold=0.3):
+def run_inference_webcam(model_path, class_names_path=None, provider="cpu", threshold=0.3, video_width=640):
     """Run real-time object detection on webcam feed."""
     # Set up providers based on selection
     if provider == "cpu":
@@ -221,6 +221,15 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
     cap = cv2.VideoCapture(0)
     if not cap.isOpened():
         raise RuntimeError("Failed to open webcam")
+    
+    # Set webcam resolution
+    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    aspect_ratio = original_height / original_width
+    new_height = int(video_width * aspect_ratio)
+    
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, video_width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, new_height)
 
     try:
         while True:
@@ -368,6 +377,12 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
     parser.add_argument(
         "--webcam", action="store_true", help="Use webcam input"
     )
+    parser.add_argument(
+        "--video-width", 
+        type=int, 
+        default=640,
+        help="Width of the video input in pixels (default: 640). Height will be adjusted to maintain aspect ratio"
+    )
     parser.add_argument(
         "--classes", type=str, help="Path to class names file (optional)"
     )
@@ -388,7 +403,13 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
     args = parser.parse_args()
 
     if args.webcam:
-        run_inference_webcam(args.model, args.classes, args.provider, args.threshold)
+        run_inference_webcam(
+            args.model, 
+            args.classes, 
+            args.provider, 
+            args.threshold,
+            args.video_width
+        )
     elif args.image:
         run_inference(args.model, args.image, args.classes, args.threshold)
     else:

From 7b93a27807336d0223bdb6f72eaa0529a669a86a Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 14:17:31 +0800
Subject: [PATCH 09/41] use cuda and trt for image inference

---
 detection_result.jpg       | Bin 60530 -> 74649 bytes
 scripts/image_inference.py |  51 +++++++++++++++++++++++++++++++++----
 2 files changed, 46 insertions(+), 5 deletions(-)

diff --git a/detection_result.jpg b/detection_result.jpg
index 00076cc022852b22a7a808aef57519dcaab55b68..74a9f557ec6107ce531c42e03fb7752fdaeb55f4 100644
GIT binary patch
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diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 32a3be7a..4c893863 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -90,10 +90,45 @@ def draw_boxes(
     return image
 
 
-def run_inference(model_path, image_path, class_names_path=None, threshold=0.3):
-    print(f"Loading ONNX model from {model_path}...")
-    session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-    print(f"Using provider: {session.get_providers()[0]}")
+def run_inference(model_path, image_path, class_names_path=None, threshold=0.3, provider="cpu"):
+    # Set up providers based on selection
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+
+    try:
+        print(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
+    except Exception as e:
+        print(f"Error creating inference session with providers {providers}: {e}")
+        print("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
 
     # Load class names if provided
     class_names = None
@@ -411,7 +446,13 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
             args.video_width
         )
     elif args.image:
-        run_inference(args.model, args.image, args.classes, args.threshold)
+        run_inference(
+            args.model, 
+            args.image, 
+            args.classes, 
+            args.threshold,
+            args.provider
+        )
     else:
         parser.error("Either --image or --webcam must be specified")
 

From 085abcceebfe6a74e5ee7e9bbd951c8658e7e4e1 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 14:27:07 +0800
Subject: [PATCH 10/41] add video inference

---
 scripts/image_inference.py | 223 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 222 insertions(+), 1 deletion(-)

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 4c893863..96a99e85 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -399,6 +399,215 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
         cv2.destroyAllWindows()
 
 
+def run_inference_video(model_path, video_path, class_names_path=None, provider="cpu", threshold=0.3, video_width=640):
+    """Run object detection on a video file."""
+    # Set up providers (same as webcam function)
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+
+    # Initialize model session
+    try:
+        print(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
+    except Exception as e:
+        print(f"Error creating inference session with providers {providers}: {e}")
+        print("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+
+    # Load class names
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            print(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            print(f"Error loading class names: {e}")
+
+    # Open video file
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise RuntimeError(f"Failed to open video file: {video_path}")
+
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Create video writer
+    output_path = "detection_output.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+    # Initialize FPS calculation
+    prev_time = time.time()
+    fps_display = 0
+    frame_count = 0
+
+    try:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frame_count += 1
+            if frame_count % 10 == 0:
+                progress = (frame_count / total_frames) * 100
+                print(f"Processing: {progress:.1f}% complete", end='\r')
+
+            # Calculate FPS
+            current_time = time.time()
+            if current_time - prev_time > 0:
+                fps_display = 1 / (current_time - prev_time)
+            prev_time = current_time
+
+            # Calculate scaling and padding using video_width parameter
+            height, width = frame.shape[:2]
+            scale = video_width / max(height, width)
+            new_height = int(height * scale)
+            new_width = int(width * scale)
+            
+            # Calculate padding
+            y_offset = (video_width - new_height) // 2
+            x_offset = (video_width - new_width) // 2
+            
+            # Create model input with padding using video_width
+            model_input = np.zeros((video_width, video_width, 3), dtype=np.uint8)
+            model_input[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = cv2.resize(frame, (new_width, new_height))
+
+            # Convert BGR to RGB for model input
+            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
+            
+            # Prepare input data
+            im_data = np.ascontiguousarray(
+                image.transpose(2, 0, 1),
+                dtype=np.float32,
+            )
+            im_data = np.expand_dims(im_data, axis=0)
+            orig_size = np.array([[video_width, video_width]], dtype=np.int64)  # Use padded size
+
+            # Run inference
+            input_name = session.get_inputs()[0].name
+            outputs = session.run(
+                output_names=None,
+                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+            )
+
+            # Process outputs
+            labels, boxes, scores = outputs
+
+            # Scale boxes from padded 640x640 to original frame size
+            boxes = boxes[0]  # Remove batch dimension
+            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
+
+            # Draw bounding boxes on the original frame
+            result_image = draw_boxes(
+                frame,  # Use original frame
+                labels[0],
+                boxes,
+                scores[0],
+                1.0,  # No additional scaling needed
+                (0, 0),  # No additional padding needed
+                threshold=threshold,
+                class_names=class_names,
+            )
+
+            # Add FPS counter and provider info (matching webcam style)
+            fps_text = f"FPS: {fps_display:.1f}"
+            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = result_image.shape[1] - text_size[0] - 10
+            text_y = 30
+
+            # Draw FPS background rectangle
+            cv2.rectangle(
+                result_image,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw FPS text
+            cv2.putText(
+                result_image,
+                fps_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
+
+            # Add provider display at bottom (matching webcam style)
+            provider_text = f"Provider: {session.get_providers()[0]}"
+            text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = (result_image.shape[1] - text_size[0]) // 2
+            text_y = result_image.shape[0] - 20
+
+            # Draw provider background rectangle
+            cv2.rectangle(
+                result_image,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw provider text
+            cv2.putText(
+                result_image,
+                provider_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
+
+            # Write frame to output video
+            out.write(result_image)
+
+            # Display frame (optional)
+            cv2.imshow('Video Detection', result_image)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+    finally:
+        cap.release()
+        out.release()
+        cv2.destroyAllWindows()
+        print(f"\nVideo processing complete. Output saved to {output_path}")
+
+
 if __name__ == "__main__":
     import argparse
 
@@ -434,6 +643,9 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
         default=0.3,
         help="Detection confidence threshold (default: 0.3)",
     )
+    parser.add_argument(
+        "--video", type=str, help="Path to input video file (optional)"
+    )
 
     args = parser.parse_args()
 
@@ -445,6 +657,15 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
             args.threshold,
             args.video_width
         )
+    elif args.video:
+        run_inference_video(
+            args.model,
+            args.video,
+            args.classes,
+            args.provider,
+            args.threshold,
+            args.video_width
+        )
     elif args.image:
         run_inference(
             args.model, 
@@ -454,6 +675,6 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
             args.provider
         )
     else:
-        parser.error("Either --image or --webcam must be specified")
+        parser.error("Either --image, --video, or --webcam must be specified")
 
     

From 141c1429f10621731187472e7b98cf14204ecb80 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 14:34:18 +0800
Subject: [PATCH 11/41] add webcam specs

---
 scripts/image_inference.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/scripts/image_inference.py b/scripts/image_inference.py
index 96a99e85..0eb0a9ee 100644
--- a/scripts/image_inference.py
+++ b/scripts/image_inference.py
@@ -257,14 +257,17 @@ def run_inference_webcam(model_path, class_names_path=None, provider="cpu", thre
     if not cap.isOpened():
         raise RuntimeError("Failed to open webcam")
     
-    # Set webcam resolution
-    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    aspect_ratio = original_height / original_width
-    new_height = int(video_width * aspect_ratio)
-    
+    # Set camera to maximum possible FPS
+    cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))  # Use MJPG format for higher FPS
+    cap.set(cv2.CAP_PROP_FPS, 1000)  # Request very high FPS - will default to max supported
     cap.set(cv2.CAP_PROP_FRAME_WIDTH, video_width)
-    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, new_height)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, int(video_width * 9/16))  # 16:9 aspect ratio
+    
+    # Print actual camera properties
+    actual_fps = cap.get(cv2.CAP_PROP_FPS)
+    actual_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    actual_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    print(f"Camera settings - FPS: {actual_fps}, Resolution: {actual_width}x{actual_height}")
 
     try:
         while True:

From aba0d1cf0fe9f32243cab69ef87794c34416b56d Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 14:54:58 +0800
Subject: [PATCH 12/41] update readme

---
 README.md                  |  42 +-
 scripts/image_inference.py | 683 --------------------------
 scripts/live_inference.py  | 983 +++++++++++++++++++++----------------
 3 files changed, 570 insertions(+), 1138 deletions(-)
 delete mode 100644 scripts/image_inference.py

diff --git a/README.md b/README.md
index 12e0b736..e231866f 100644
--- a/README.md
+++ b/README.md
@@ -250,40 +250,38 @@ If you would like to use the CUDA backend, you can install the `onnxruntime-gpu`
 For video inference, specify the path to the video file as the input. Output video will be saved as `onnx_result.mp4` in the current directory.
 
 ```bash
-python scripts/live_inference.py 
-    --onnx model.onnx           # Path to the ONNX model file
+python scripts/live_inference.py \
+    --model model.onnx           # Path to the ONNX model file
     --input video.mp4           # Path to the input video file
-    --class-names classes.txt   # Path to the classes file with each name on a new row
-    --input-size 320            # Input size for the model
+    --classes classes.txt       # Path to the classes file with each name on a new row
+    --video-width 320          # Input size for the model
+    --provider cpu             # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3           # Detection confidence threshold
 ```
 
-The following is a demo of video inference after training for about 50 epochs on the vehicles dataset with image size 320x320.
-
-https://github.com/user-attachments/assets/5066768f-c97e-4999-af81-ffd29d88f529
-
-
-You can also run live inference on a webcam by setting the `webcam` flag.
+You can also run live inference on a webcam by setting the `--webcam` flag.
 
 ```bash
-python scripts/live_inference.py 
-    --onnx model.onnx           # Path to the ONNX model file
+python scripts/live_inference.py \
+    --model model.onnx          # Path to the ONNX model file
     --webcam                    # Use webcam as input source
-    --class-names classes.txt   # Path to the classes file. Each class name should be on a new line.
-    --input-size 320            # Input size for the model
+    --classes classes.txt       # Path to the classes file. Each class name should be on a new line
+    --video-width 720          # Input size for the model
+    --provider tensorrt        # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3           # Detection confidence threshold
 ```
-The following is a demo of webcam inference after training on the rock paper scissors dataset 640x640 resolution image.
-
-https://github.com/user-attachments/assets/6e5dbb15-4e3a-45a3-997e-157bb9370146
-
 
 For image inference, specify the path to the image file as the input.
+
 ```bash
-python scripts/live_inference.py 
-    --onnx model.onnx           # Path to the ONNX model file
+python scripts/live_inference.py \
+    --model model.onnx          # Path to the ONNX model file
     --input image.jpg           # Path to the input image file
-    --class-names classes.txt   # Path to the classes file. Each class name should be on a new line.
-    --input-size 320            # Input size for the model
+    --classes classes.txt       # Path to the classes file. Each class name should be on a new line
+    --provider cpu             # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3           # Detection confidence threshold
 ```
+
 The following is a demo of image inference
 
 ![image](assets/sample_result_image_1.jpg)
diff --git a/scripts/image_inference.py b/scripts/image_inference.py
deleted file mode 100644
index 0eb0a9ee..00000000
--- a/scripts/image_inference.py
+++ /dev/null
@@ -1,683 +0,0 @@
-import colorsys
-import time
-import torch
-import cv2
-import numpy as np
-import onnxruntime as ort
-
-
-def generate_colors(num_classes):
-    """Generate distinct colors for visualization."""
-    hsv_tuples = [(x / num_classes, 0.8, 0.9) for x in range(num_classes)]
-    colors = []
-    for hsv in hsv_tuples:
-        rgb = colorsys.hsv_to_rgb(*hsv)
-        colors.append(tuple(int(255 * x) for x in rgb))
-    return colors
-
-
-def draw_boxes(
-    image, labels, boxes, scores, ratio, padding, threshold=0.3, class_names=None
-):
-    """Draw bounding boxes on the image."""
-    # Generate colors for classes
-    num_classes = len(class_names) if class_names else 91
-    colors = generate_colors(num_classes)
-
-    # Filter detections by threshold
-    valid_indices = scores > threshold
-    labels = labels[valid_indices]
-    boxes = boxes[valid_indices]
-    scores = scores[valid_indices]
-
-    for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
-        # Get color for this class
-        class_idx = int(lbl)
-        color = colors[class_idx % len(colors)]
-
-        # Use box coordinates directly
-        box_coords = [
-            int(box[0]),  # x1
-            int(box[1]),  # y1
-            int(box[2]),  # x2
-            int(box[3]),  # y2
-        ]
-
-        # Draw rectangle
-        cv2.rectangle(
-            image,
-            (box_coords[0], box_coords[1]),
-            (box_coords[2], box_coords[3]),
-            color,
-            2,
-        )
-
-        # Prepare label text
-        if class_names and class_idx < len(class_names):
-            label_text = f"{class_names[class_idx]} {score:.2f}"
-        else:
-            label_text = f"Class {class_idx} {score:.2f}"
-
-        # Get text size
-        (text_width, text_height), _ = cv2.getTextSize(
-            label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
-        )
-
-        # Draw text background
-        cv2.rectangle(
-            image,
-            (box_coords[0], box_coords[1] - text_height - 4),
-            (box_coords[0] + text_width + 4, box_coords[1]),
-            color,
-            -1,  # Filled rectangle
-        )
-
-        # Calculate text color based on background brightness
-        brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
-        text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
-
-        # Draw text
-        cv2.putText(
-            image,
-            label_text,
-            (box_coords[0] + 2, box_coords[1] - 2),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.7,
-            text_color,
-            2,
-        )
-
-    return image
-
-
-def run_inference(model_path, image_path, class_names_path=None, threshold=0.3, provider="cpu"):
-    # Set up providers based on selection
-    if provider == "cpu":
-        providers = ["CPUExecutionProvider"]
-    elif provider == "cuda":
-        providers = [
-            (
-                "CUDAExecutionProvider",
-                {
-                    "arena_extend_strategy": "kNextPowerOfTwo",
-                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
-                    "cudnn_conv_algo_search": "EXHAUSTIVE",
-                    "do_copy_in_default_stream": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-    elif provider == "tensorrt":
-        providers = [
-            (
-                "TensorrtExecutionProvider",
-                {
-                    "trt_fp16_enable": False,
-                    "trt_engine_cache_enable": True,
-                    "trt_engine_cache_path": "./trt_cache",
-                    "trt_timing_cache_enable": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-
-    try:
-        print(f"Loading ONNX model with providers: {providers}...")
-        session = ort.InferenceSession(model_path, providers=providers)
-        print(f"Using provider: {session.get_providers()[0]}")
-    except Exception as e:
-        print(f"Error creating inference session with providers {providers}: {e}")
-        print("Attempting to fall back to CPU execution...")
-        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-
-    # Load class names if provided
-    class_names = None
-    if class_names_path:
-        try:
-            with open(class_names_path, "r") as f:
-                class_names = [line.strip() for line in f.readlines()]
-            print(f"Loaded {len(class_names)} class names")
-        except Exception as e:
-            print(f"Error loading class names: {e}")
-
-    # Load image
-    image = cv2.imread(image_path)  # Load as BGR
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
-    original_image = image.copy()
-
-    im_data = np.ascontiguousarray(
-        image.transpose(2, 0, 1),  # HWC to CHW format
-        dtype=np.float32,
-    )
-    im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
-    orig_size = np.array([[image.shape[0], image.shape[1]]], dtype=np.int64)
-
-    print(f"Image frame shape: {image.shape}")
-    print(f"Processed input shape: {im_data.shape}")
-
-    # Get input name from model metadata
-    input_name = session.get_inputs()[0].name
-
-    # Run inference
-    outputs = session.run(
-        output_names=None,
-        input_feed={input_name: im_data, "orig_target_sizes": orig_size},
-    )
-
-    # Process outputs
-    labels, boxes, scores = outputs
-
-    # print(outputs)
-
-    # Draw bounding boxes on the image
-    result_image = draw_boxes(
-        original_image,
-        labels[0],
-        boxes[0],
-        scores[0],
-        1.0,  # No ratio needed since we're not resizing
-        (0, 0),  # No padding needed
-        threshold=threshold,
-        class_names=class_names,
-    )
-
-    # Save and show result
-    output_path = "detection_result.jpg"
-    result_bgr = cv2.cvtColor(
-        result_image, cv2.COLOR_RGB2BGR
-    )  # Convert back to BGR for OpenCV
-    cv2.imwrite(output_path, result_bgr)
-    print(f"Detection complete. Result saved to {output_path}")
-
-    # Display the result
-    cv2.imshow("Detection Result", result_bgr)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-    return result_image
-
-
-def run_inference_webcam(model_path, class_names_path=None, provider="cpu", threshold=0.3, video_width=640):
-    """Run real-time object detection on webcam feed."""
-    # Set up providers based on selection
-    if provider == "cpu":
-        providers = ["CPUExecutionProvider"]
-    elif provider == "cuda":
-        providers = [
-            (
-                "CUDAExecutionProvider",
-                {
-                    "arena_extend_strategy": "kNextPowerOfTwo",
-                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
-                    "cudnn_conv_algo_search": "EXHAUSTIVE",
-                    "do_copy_in_default_stream": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-    elif provider == "tensorrt":
-        providers = [
-            (
-                "TensorrtExecutionProvider",
-                {
-                    "trt_fp16_enable": False,
-                    "trt_engine_cache_enable": True,
-                    "trt_engine_cache_path": "./trt_cache",
-                    "trt_timing_cache_enable": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-
-    try:
-        print(f"Loading ONNX model with providers: {providers}...")
-        session = ort.InferenceSession(model_path, providers=providers)
-        print(f"Using provider: {session.get_providers()[0]}")
-    except Exception as e:
-        print(f"Error creating inference session with providers {providers}: {e}")
-        print("Attempting to fall back to CPU execution...")
-        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-
-    # Update FPS calculation variables
-    prev_time = time.time()
-    fps_display = 0
-    
-    # Load class names if provided
-    class_names = None
-    if class_names_path:
-        try:
-            with open(class_names_path, "r") as f:
-                class_names = [line.strip() for line in f.readlines()]
-            print(f"Loaded {len(class_names)} class names")
-        except Exception as e:
-            print(f"Error loading class names: {e}")
-
-    # Initialize webcam
-    cap = cv2.VideoCapture(0)
-    if not cap.isOpened():
-        raise RuntimeError("Failed to open webcam")
-    
-    # Set camera to maximum possible FPS
-    cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))  # Use MJPG format for higher FPS
-    cap.set(cv2.CAP_PROP_FPS, 1000)  # Request very high FPS - will default to max supported
-    cap.set(cv2.CAP_PROP_FRAME_WIDTH, video_width)
-    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, int(video_width * 9/16))  # 16:9 aspect ratio
-    
-    # Print actual camera properties
-    actual_fps = cap.get(cv2.CAP_PROP_FPS)
-    actual_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-    actual_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-    print(f"Camera settings - FPS: {actual_fps}, Resolution: {actual_width}x{actual_height}")
-
-    try:
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                print("Failed to grab frame")
-                break
-
-            # Calculate FPS
-            current_time = time.time()
-            if current_time - prev_time > 0:  # Avoid division by zero
-                fps_display = 1 / (current_time - prev_time)
-            prev_time = current_time
-
-            # Calculate scaling and padding
-            height, width = frame.shape[:2]
-            scale = 640.0 / max(height, width)
-            new_height = int(height * scale)
-            new_width = int(width * scale)
-            
-            # Calculate padding
-            y_offset = (640 - new_height) // 2
-            x_offset = (640 - new_width) // 2
-            
-            # Create model input with padding
-            model_input = np.zeros((640, 640, 3), dtype=np.uint8)
-            model_input[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = cv2.resize(frame, (new_width, new_height))
-
-            # Convert BGR to RGB for model input
-            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
-            
-            # Prepare input data
-            im_data = np.ascontiguousarray(
-                image.transpose(2, 0, 1),
-                dtype=np.float32,
-            )
-            im_data = np.expand_dims(im_data, axis=0)
-            orig_size = np.array([[640, 640]], dtype=np.int64)  # Use padded size
-
-            # Get input name and run inference
-            input_name = session.get_inputs()[0].name
-            outputs = session.run(
-                output_names=None,
-                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
-            )
-
-            # Process outputs
-            labels, boxes, scores = outputs
-
-            # Scale boxes from padded 640x640 to original frame size
-            boxes = boxes[0]  # Remove batch dimension
-            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
-            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
-
-            # Draw bounding boxes on the original frame
-            result_image = draw_boxes(
-                frame,  # Use original frame
-                labels[0],
-                boxes,
-                scores[0],
-                1.0,  # No additional scaling needed
-                (0, 0),  # No additional padding needed
-                threshold=threshold,
-                class_names=class_names,
-            )
-
-            # No need to convert back to BGR since we're using the original frame
-            result_bgr = result_image
-
-            # Add FPS and provider display
-            fps_text = f"FPS: {fps_display:.1f}"
-            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-            text_x = result_bgr.shape[1] - text_size[0] - 10
-            text_y = 30
-
-            # Draw FPS background rectangle
-            cv2.rectangle(
-                result_bgr,
-                (text_x - 5, text_y - text_size[1] - 5),
-                (text_x + text_size[0] + 5, text_y + 5),
-                (139, 0, 0),
-                -1,
-            )
-
-            # Draw FPS text
-            cv2.putText(
-                result_bgr,
-                fps_text,
-                (text_x, text_y),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.8,
-                (255, 255, 255),
-                2,
-            )
-
-            # Add provider display
-            provider_text = f"Provider: {session.get_providers()[0]}"
-            text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-            text_x = (result_bgr.shape[1] - text_size[0]) // 2
-            text_y = result_bgr.shape[0] - 20
-
-            # Draw provider background rectangle
-            cv2.rectangle(
-                result_bgr,
-                (text_x - 5, text_y - text_size[1] - 5),
-                (text_x + text_size[0] + 5, text_y + 5),
-                (139, 0, 0),
-                -1,
-            )
-
-            # Draw provider text
-            cv2.putText(
-                result_bgr,
-                provider_text,
-                (text_x, text_y),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.8,
-                (255, 255, 255),
-                2,
-            )
-
-            # Display the result
-            cv2.imshow("Webcam Detection", result_bgr)
-
-            # Handle key presses
-            key = cv2.waitKey(1) & 0xFF
-            if key == ord('q'):
-                break
-
-    finally:
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def run_inference_video(model_path, video_path, class_names_path=None, provider="cpu", threshold=0.3, video_width=640):
-    """Run object detection on a video file."""
-    # Set up providers (same as webcam function)
-    if provider == "cpu":
-        providers = ["CPUExecutionProvider"]
-    elif provider == "cuda":
-        providers = [
-            (
-                "CUDAExecutionProvider",
-                {
-                    "arena_extend_strategy": "kNextPowerOfTwo",
-                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
-                    "cudnn_conv_algo_search": "EXHAUSTIVE",
-                    "do_copy_in_default_stream": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-    elif provider == "tensorrt":
-        providers = [
-            (
-                "TensorrtExecutionProvider",
-                {
-                    "trt_fp16_enable": False,
-                    "trt_engine_cache_enable": True,
-                    "trt_engine_cache_path": "./trt_cache",
-                    "trt_timing_cache_enable": True,
-                },
-            ),
-            "CPUExecutionProvider",
-        ]
-
-    # Initialize model session
-    try:
-        print(f"Loading ONNX model with providers: {providers}...")
-        session = ort.InferenceSession(model_path, providers=providers)
-        print(f"Using provider: {session.get_providers()[0]}")
-    except Exception as e:
-        print(f"Error creating inference session with providers {providers}: {e}")
-        print("Attempting to fall back to CPU execution...")
-        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-
-    # Load class names
-    class_names = None
-    if class_names_path:
-        try:
-            with open(class_names_path, "r") as f:
-                class_names = [line.strip() for line in f.readlines()]
-            print(f"Loaded {len(class_names)} class names")
-        except Exception as e:
-            print(f"Error loading class names: {e}")
-
-    # Open video file
-    cap = cv2.VideoCapture(video_path)
-    if not cap.isOpened():
-        raise RuntimeError(f"Failed to open video file: {video_path}")
-
-    # Get video properties
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    # Create video writer
-    output_path = "detection_output.mp4"
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
-
-    # Initialize FPS calculation
-    prev_time = time.time()
-    fps_display = 0
-    frame_count = 0
-
-    try:
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            frame_count += 1
-            if frame_count % 10 == 0:
-                progress = (frame_count / total_frames) * 100
-                print(f"Processing: {progress:.1f}% complete", end='\r')
-
-            # Calculate FPS
-            current_time = time.time()
-            if current_time - prev_time > 0:
-                fps_display = 1 / (current_time - prev_time)
-            prev_time = current_time
-
-            # Calculate scaling and padding using video_width parameter
-            height, width = frame.shape[:2]
-            scale = video_width / max(height, width)
-            new_height = int(height * scale)
-            new_width = int(width * scale)
-            
-            # Calculate padding
-            y_offset = (video_width - new_height) // 2
-            x_offset = (video_width - new_width) // 2
-            
-            # Create model input with padding using video_width
-            model_input = np.zeros((video_width, video_width, 3), dtype=np.uint8)
-            model_input[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = cv2.resize(frame, (new_width, new_height))
-
-            # Convert BGR to RGB for model input
-            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
-            
-            # Prepare input data
-            im_data = np.ascontiguousarray(
-                image.transpose(2, 0, 1),
-                dtype=np.float32,
-            )
-            im_data = np.expand_dims(im_data, axis=0)
-            orig_size = np.array([[video_width, video_width]], dtype=np.int64)  # Use padded size
-
-            # Run inference
-            input_name = session.get_inputs()[0].name
-            outputs = session.run(
-                output_names=None,
-                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
-            )
-
-            # Process outputs
-            labels, boxes, scores = outputs
-
-            # Scale boxes from padded 640x640 to original frame size
-            boxes = boxes[0]  # Remove batch dimension
-            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
-            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
-
-            # Draw bounding boxes on the original frame
-            result_image = draw_boxes(
-                frame,  # Use original frame
-                labels[0],
-                boxes,
-                scores[0],
-                1.0,  # No additional scaling needed
-                (0, 0),  # No additional padding needed
-                threshold=threshold,
-                class_names=class_names,
-            )
-
-            # Add FPS counter and provider info (matching webcam style)
-            fps_text = f"FPS: {fps_display:.1f}"
-            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-            text_x = result_image.shape[1] - text_size[0] - 10
-            text_y = 30
-
-            # Draw FPS background rectangle
-            cv2.rectangle(
-                result_image,
-                (text_x - 5, text_y - text_size[1] - 5),
-                (text_x + text_size[0] + 5, text_y + 5),
-                (139, 0, 0),
-                -1,
-            )
-
-            # Draw FPS text
-            cv2.putText(
-                result_image,
-                fps_text,
-                (text_x, text_y),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.8,
-                (255, 255, 255),
-                2,
-            )
-
-            # Add provider display at bottom (matching webcam style)
-            provider_text = f"Provider: {session.get_providers()[0]}"
-            text_size = cv2.getTextSize(provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-            text_x = (result_image.shape[1] - text_size[0]) // 2
-            text_y = result_image.shape[0] - 20
-
-            # Draw provider background rectangle
-            cv2.rectangle(
-                result_image,
-                (text_x - 5, text_y - text_size[1] - 5),
-                (text_x + text_size[0] + 5, text_y + 5),
-                (139, 0, 0),
-                -1,
-            )
-
-            # Draw provider text
-            cv2.putText(
-                result_image,
-                provider_text,
-                (text_x, text_y),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.8,
-                (255, 255, 255),
-                2,
-            )
-
-            # Write frame to output video
-            out.write(result_image)
-
-            # Display frame (optional)
-            cv2.imshow('Video Detection', result_image)
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                break
-
-    finally:
-        cap.release()
-        out.release()
-        cv2.destroyAllWindows()
-        print(f"\nVideo processing complete. Output saved to {output_path}")
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(description="Simple ONNX object detection")
-    parser.add_argument(
-        "--model", type=str, required=True, help="Path to ONNX model file"
-    )
-    parser.add_argument(
-        "--image", type=str, help="Path to input image (optional)"
-    )
-    parser.add_argument(
-        "--webcam", action="store_true", help="Use webcam input"
-    )
-    parser.add_argument(
-        "--video-width", 
-        type=int, 
-        default=640,
-        help="Width of the video input in pixels (default: 640). Height will be adjusted to maintain aspect ratio"
-    )
-    parser.add_argument(
-        "--classes", type=str, help="Path to class names file (optional)"
-    )
-    parser.add_argument(
-        "--provider",
-        type=str,
-        choices=["cpu", "cuda", "tensorrt"],
-        default="cpu",
-        help="ONNXRuntime provider to use for inference",
-    )
-    parser.add_argument(
-        "--threshold",
-        type=float,
-        default=0.3,
-        help="Detection confidence threshold (default: 0.3)",
-    )
-    parser.add_argument(
-        "--video", type=str, help="Path to input video file (optional)"
-    )
-
-    args = parser.parse_args()
-
-    if args.webcam:
-        run_inference_webcam(
-            args.model, 
-            args.classes, 
-            args.provider, 
-            args.threshold,
-            args.video_width
-        )
-    elif args.video:
-        run_inference_video(
-            args.model,
-            args.video,
-            args.classes,
-            args.provider,
-            args.threshold,
-            args.video_width
-        )
-    elif args.image:
-        run_inference(
-            args.model, 
-            args.image, 
-            args.classes, 
-            args.threshold,
-            args.provider
-        )
-    else:
-        parser.error("Either --image, --video, or --webcam must be specified")
-
-    
diff --git a/scripts/live_inference.py b/scripts/live_inference.py
index c9930824..7addda79 100644
--- a/scripts/live_inference.py
+++ b/scripts/live_inference.py
@@ -4,291 +4,372 @@
 import cv2
 import numpy as np
 import onnxruntime as ort
-import torch  # Do not remove this lf you are using the CUDA or TensortEP. Weird bug - https://github.com/microsoft/onnxruntime/issues/11092
-from PIL import Image, ImageDraw
-from tqdm import tqdm
 
-
-def resize_with_aspect_ratio(image, size, interpolation=Image.BILINEAR):
-    """Resizes an image while maintaining aspect ratio and pads it."""
-    original_width, original_height = image.size
-    ratio = min(size / original_width, size / original_height)
-    new_width = int(original_width * ratio)
-    new_height = int(original_height * ratio)
-    image = image.resize((new_width, new_height), interpolation)
-
-    # Create a new image with the desired size and paste the resized image onto it
-    new_image = Image.new("RGB", (size, size))
-    new_image.paste(image, ((size - new_width) // 2, (size - new_height) // 2))
-    return new_image, ratio, (size - new_width) // 2, (size - new_height) // 2
+ort.preload_dlls()
 
 
 def generate_colors(num_classes):
-    """Generate a list of distinct colors for different classes."""
-    # Generate evenly spaced hues
+    """Generate distinct colors for visualization."""
     hsv_tuples = [(x / num_classes, 0.8, 0.9) for x in range(num_classes)]
-
-    # Convert to RGB
     colors = []
     for hsv in hsv_tuples:
         rgb = colorsys.hsv_to_rgb(*hsv)
-        # Convert to 0-255 range and to tuple
         colors.append(tuple(int(255 * x) for x in rgb))
-
     return colors
 
 
-def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.3, class_names=None):
-    result_images = []
-
+def draw_boxes(
+    image, labels, boxes, scores, ratio, padding, threshold=0.3, class_names=None
+):
+    """Draw bounding boxes on the image."""
     # Generate colors for classes
-    num_classes = (
-        len(class_names) if class_names else 91
-    )  # Use length of class_names if available, otherwise default to COCO's 91 classes
+    num_classes = len(class_names) if class_names else 91
     colors = generate_colors(num_classes)
 
-    for i, im in enumerate(images):
-        draw = ImageDraw.Draw(im)
-        scr = scores[i]
-        lab = labels[i][scr > thrh]
-        box = boxes[i][scr > thrh]
-        scr = scr[scr > thrh]
-
-        ratio = ratios[i]
-        pad_w, pad_h = paddings[i]
-
-        for j, (lbl, bb) in enumerate(zip(lab, box)):
-            # Get color for this class
-            class_idx = int(lbl)
-            color = colors[class_idx % len(colors)]
-
-            # Convert RGB to hex for PIL
-            hex_color = "#{:02x}{:02x}{:02x}".format(*color)
-
-            # Adjust bounding boxes according to the resizing and padding
-            bb = [
-                (bb[0] - pad_w) / ratio,
-                (bb[1] - pad_h) / ratio,
-                (bb[2] - pad_w) / ratio,
-                (bb[3] - pad_h) / ratio,
-            ]
-
-            # Draw rectangle with class-specific color
-            draw.rectangle(bb, outline=hex_color, width=3)
-
-            # Use class name if available, otherwise use class index
-            if class_names and class_idx < len(class_names):
-                label_text = f"{class_names[class_idx]} {scr[j]:.2f}"
-            else:
-                label_text = f"Class {class_idx} {scr[j]:.2f}"
-
-            # Draw text background
-            text_size = draw.textbbox((0, 0), label_text, font=None)
-            text_width = text_size[2] - text_size[0]
-            text_height = text_size[3] - text_size[1]
-
-            # Draw text background rectangle
-            draw.rectangle(
-                [bb[0], bb[1] - text_height - 4, bb[0] + text_width + 4, bb[1]],
-                fill=hex_color,
-            )
+    # Filter detections by threshold
+    valid_indices = scores > threshold
+    labels = labels[valid_indices]
+    boxes = boxes[valid_indices]
+    scores = scores[valid_indices]
+
+    for j, (lbl, box, score) in enumerate(zip(labels, boxes, scores)):
+        # Get color for this class
+        class_idx = int(lbl)
+        color = colors[class_idx % len(colors)]
+
+        # Use box coordinates directly
+        box_coords = [
+            int(box[0]),  # x1
+            int(box[1]),  # y1
+            int(box[2]),  # x2
+            int(box[3]),  # y2
+        ]
+
+        # Draw rectangle
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1]),
+            (box_coords[2], box_coords[3]),
+            color,
+            2,
+        )
+
+        # Prepare label text
+        if class_names and class_idx < len(class_names):
+            label_text = f"{class_names[class_idx]} {score:.2f}"
+        else:
+            label_text = f"Class {class_idx} {score:.2f}"
 
-            # Draw text in white or black depending on color brightness
-            brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
-            text_color = "black" if brightness > 128 else "white"
+        # Get text size
+        (text_width, text_height), _ = cv2.getTextSize(
+            label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
+        )
 
-            # Draw text
-            draw.text(
-                (bb[0] + 2, bb[1] - text_height - 2), text=label_text, fill=text_color
-            )
+        # Draw text background
+        cv2.rectangle(
+            image,
+            (box_coords[0], box_coords[1] - text_height - 4),
+            (box_coords[0] + text_width + 4, box_coords[1]),
+            color,
+            -1,  # Filled rectangle
+        )
+
+        # Calculate text color based on background brightness
+        brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
+        text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
+
+        # Draw text
+        cv2.putText(
+            image,
+            label_text,
+            (box_coords[0] + 2, box_coords[1] - 2),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.7,
+            text_color,
+            2,
+        )
+
+    return image
+
+
+def run_inference(
+    model_path, image_path, class_names_path=None, threshold=0.3, provider="cpu"
+):
+    # Set up providers based on selection
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+
+    try:
+        print(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
+    except Exception as e:
+        print(f"Error creating inference session with providers {providers}: {e}")
+        print("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
 
-        result_images.append(im)
-    return result_images
+    # Load class names if provided
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            print(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            print(f"Error loading class names: {e}")
 
+    # Load image
+    image = cv2.imread(image_path)  # Load as BGR
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+    original_image = image.copy()
 
-def process_image(sess, im_pil, class_names=None, input_size=640):
-    # Resize image while preserving aspect ratio
-    resized_im_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(im_pil, input_size)
-    orig_size = np.array(
-        [[resized_im_pil.size[1], resized_im_pil.size[0]]], dtype=np.int64
+    im_data = np.ascontiguousarray(
+        image.transpose(2, 0, 1),  # HWC to CHW format
+        dtype=np.float32,
     )
+    im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
+    orig_size = np.array([[image.shape[0], image.shape[1]]], dtype=np.int64)
+
+    print(f"Image frame shape: {image.shape}")
+    print(f"Processed input shape: {im_data.shape}")
 
     # Get input name from model metadata
-    input_name = sess.get_inputs()[0].name
-    is_preprocessed = input_name == "input_bgr"
-
-    if is_preprocessed:
-        # For models with preprocessing, input should be BGR float32 format
-        im_data = cv2.cvtColor(np.array(resized_im_pil), cv2.COLOR_RGB2BGR)
-        im_data = im_data.astype(np.float32)  # Convert to float32
-        im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
-    else:
-        # For models without preprocessing, normalize to 0-1 range
-        im_data = np.array(resized_im_pil, dtype=np.float32) / 255.0
-        im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
+    input_name = session.get_inputs()[0].name
 
-    output = sess.run(
+    # Run inference
+    outputs = session.run(
         output_names=None,
         input_feed={input_name: im_data, "orig_target_sizes": orig_size},
     )
 
-    labels, boxes, scores = output
+    # Process outputs
+    labels, boxes, scores = outputs
 
-    result_images = draw(
-        [im_pil],
-        labels,
-        boxes,
-        scores,
-        [ratio],
-        [(pad_w, pad_h)],
+    # print(outputs)
+
+    # Draw bounding boxes on the image
+    result_image = draw_boxes(
+        original_image,
+        labels[0],
+        boxes[0],
+        scores[0],
+        1.0,  # No ratio needed since we're not resizing
+        (0, 0),  # No padding needed
+        threshold=threshold,
         class_names=class_names,
     )
-    filename = "onnx_result.jpg"
-    result_images[0].save(filename)
-    print(f"Image processing complete. Result saved as '{filename}'.")
 
-    image = cv2.imread(filename)
-    cv2.imshow("Image", image)
+    # Save and show result
+    output_path = "detection_result.jpg"
+    result_bgr = cv2.cvtColor(
+        result_image, cv2.COLOR_RGB2BGR
+    )  # Convert back to BGR for OpenCV
+    cv2.imwrite(output_path, result_bgr)
+    print(f"Detection complete. Result saved to {output_path}")
+
+    # Display the result
+    cv2.imshow("Detection Result", result_bgr)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
 
+    return result_image
 
-def process_video(sess, video_path, class_names=None, input_size=640):
-    cap = cv2.VideoCapture(video_path)
-
-    # Get video properties
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    # Define the codec and create VideoWriter object
-    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-    out = cv2.VideoWriter("onnx_result.mp4", fourcc, fps, (orig_w, orig_h))
 
-    print("Processing video frames...")
-    progress_bar = tqdm(total=total_frames, desc="Processing frames", unit="frames")
+def run_inference_webcam(
+    model_path, class_names_path=None, provider="cpu", threshold=0.3, video_width=640
+):
+    """Run real-time object detection on webcam feed."""
+    # Set up providers based on selection
+    if provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+    elif provider == "cuda":
+        providers = [
+            (
+                "CUDAExecutionProvider",
+                {
+                    "arena_extend_strategy": "kNextPowerOfTwo",
+                    "gpu_mem_limit": 2 * 1024 * 1024 * 1024,
+                    "cudnn_conv_algo_search": "EXHAUSTIVE",
+                    "do_copy_in_default_stream": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
+    elif provider == "tensorrt":
+        providers = [
+            (
+                "TensorrtExecutionProvider",
+                {
+                    "trt_fp16_enable": False,
+                    "trt_engine_cache_enable": True,
+                    "trt_engine_cache_path": "./trt_cache",
+                    "trt_timing_cache_enable": True,
+                },
+            ),
+            "CPUExecutionProvider",
+        ]
 
-    # Create a simple window for displaying the video
-    window_name = "Video Detection"
-    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
-    cv2.resizeWindow(window_name, min(orig_w, 1280), min(orig_h, 720))
+    try:
+        print(f"Loading ONNX model with providers: {providers}...")
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
+    except Exception as e:
+        print(f"Error creating inference session with providers {providers}: {e}")
+        print("Attempting to fall back to CPU execution...")
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
 
-    # Variables for FPS calculation
+    # Update FPS calculation variables
     prev_time = time.time()
-    curr_time = 0
     fps_display = 0
 
-    # Add provider display flag and get actual provider name
-    show_provider = True
-    provider = sess.get_providers()[0]  # Get the first active provider
-
-    # Get input name from model metadata
-    input_name = sess.get_inputs()[0].name
-    is_preprocessed = input_name == "input_bgr"
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Calculate FPS
-        curr_time = time.time()
-        if curr_time - prev_time > 0:  # Avoid division by zero
-            fps_display = 1 / (curr_time - prev_time)
-        prev_time = curr_time
-
-        # Convert frame to PIL image
-        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-        # Resize frame while preserving aspect ratio
-        resized_frame_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(
-            frame_pil, input_size
-        )
-        orig_size = np.array(
-            [[resized_frame_pil.size[1], resized_frame_pil.size[0]]], dtype=np.int64
-        )
+    # Load class names if provided
+    class_names = None
+    if class_names_path:
+        try:
+            with open(class_names_path, "r") as f:
+                class_names = [line.strip() for line in f.readlines()]
+            print(f"Loaded {len(class_names)} class names")
+        except Exception as e:
+            print(f"Error loading class names: {e}")
 
-        if is_preprocessed:
-            # For models with preprocessing, input should be BGR float32 format
-            im_data = cv2.cvtColor(np.array(resized_frame_pil), cv2.COLOR_RGB2BGR)
-            im_data = im_data.astype(np.float32)  # Convert to float32
-            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
-        else:
-            # For models without preprocessing, normalize to 0-1 range
-            im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
-            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
+    # Initialize webcam
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        raise RuntimeError("Failed to open webcam")
+
+    # Set camera to maximum possible FPS
+    cap.set(
+        cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc("M", "J", "P", "G")
+    )  # Use MJPG format for higher FPS
+    cap.set(
+        cv2.CAP_PROP_FPS, 1000
+    )  # Request very high FPS - will default to max supported
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, video_width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, int(video_width * 9 / 16))  # 16:9 aspect ratio
+
+    # Print actual camera properties
+    actual_fps = cap.get(cv2.CAP_PROP_FPS)
+    actual_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    actual_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    print(
+        f"Camera settings - FPS: {actual_fps}, Resolution: {actual_width}x{actual_height}"
+    )
 
-        output = sess.run(
-            output_names=None,
-            input_feed={input_name: im_data, "orig_target_sizes": orig_size},
-        )
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                print("Failed to grab frame")
+                break
+
+            # Calculate FPS
+            current_time = time.time()
+            if current_time - prev_time > 0:  # Avoid division by zero
+                fps_display = 1 / (current_time - prev_time)
+            prev_time = current_time
+
+            # Calculate scaling and padding
+            height, width = frame.shape[:2]
+            scale = 640.0 / max(height, width)
+            new_height = int(height * scale)
+            new_width = int(width * scale)
+
+            # Calculate padding
+            y_offset = (640 - new_height) // 2
+            x_offset = (640 - new_width) // 2
+
+            # Create model input with padding
+            model_input = np.zeros((640, 640, 3), dtype=np.uint8)
+            model_input[
+                y_offset : y_offset + new_height, x_offset : x_offset + new_width
+            ] = cv2.resize(frame, (new_width, new_height))
+
+            # Convert BGR to RGB for model input
+            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
+
+            # Prepare input data
+            im_data = np.ascontiguousarray(
+                image.transpose(2, 0, 1),
+                dtype=np.float32,
+            )
+            im_data = np.expand_dims(im_data, axis=0)
+            orig_size = np.array([[640, 640]], dtype=np.int64)  # Use padded size
+
+            # Get input name and run inference
+            input_name = session.get_inputs()[0].name
+            outputs = session.run(
+                output_names=None,
+                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+            )
 
-        labels, boxes, scores = output
-
-        # Draw detections on the original frame
-        result_images = draw(
-            [frame_pil],
-            labels,
-            boxes,
-            scores,
-            [ratio],
-            [(pad_w, pad_h)],
-            class_names=class_names,
-        )
-        frame_with_detections = result_images[0]
+            # Process outputs
+            labels, boxes, scores = outputs
+
+            # Scale boxes from padded 640x640 to original frame size
+            boxes = boxes[0]  # Remove batch dimension
+            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
+
+            # Draw bounding boxes on the original frame
+            result_image = draw_boxes(
+                frame,  # Use original frame
+                labels[0],
+                boxes,
+                scores[0],
+                1.0,  # No additional scaling needed
+                (0, 0),  # No additional padding needed
+                threshold=threshold,
+                class_names=class_names,
+            )
 
-        # Convert back to OpenCV image
-        display_frame = cv2.cvtColor(np.array(frame_with_detections), cv2.COLOR_RGB2BGR)
+            # No need to convert back to BGR since we're using the original frame
+            result_bgr = result_image
 
-        # Add FPS text to the top right corner with dark blue background
-        fps_text = f"FPS: {fps_display:.1f}"
-        text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-        text_x = display_frame.shape[1] - text_size[0] - 10
-        text_y = 30
+            # Add FPS and provider display
+            fps_text = f"FPS: {fps_display:.1f}"
+            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = result_bgr.shape[1] - text_size[0] - 10
+            text_y = 30
 
-        # Draw background rectangle
-        cv2.rectangle(
-            display_frame,
-            (text_x - 5, text_y - text_size[1] - 5),
-            (text_x + text_size[0] + 5, text_y + 5),
-            (139, 0, 0),
-            -1,
-        )  # Dark blue background (BGR format)
-
-        # Draw text in white
-        cv2.putText(
-            display_frame,
-            fps_text,
-            (text_x, text_y),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.8,
-            (255, 255, 255),
-            2,
-        )
-
-        # Add provider text at the bottom center when show_provider is True
-        if show_provider:
-            provider_text = f"Provider: {provider}"
-            text_size = cv2.getTextSize(
-                provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
-            )[0]
-            text_x = (display_frame.shape[1] - text_size[0]) // 2
-            text_y = display_frame.shape[0] - 20
-
-            # Draw background rectangle
+            # Draw FPS background rectangle
             cv2.rectangle(
-                display_frame,
+                result_bgr,
                 (text_x - 5, text_y - text_size[1] - 5),
                 (text_x + text_size[0] + 5, text_y + 5),
                 (139, 0, 0),
                 -1,
-            )  # Dark blue background
+            )
 
-            # Draw text in white
+            # Draw FPS text
             cv2.putText(
-                display_frame,
-                provider_text,
+                result_bgr,
+                fps_text,
                 (text_x, text_y),
                 cv2.FONT_HERSHEY_SIMPLEX,
                 0.8,
@@ -296,153 +377,26 @@ def process_video(sess, video_path, class_names=None, input_size=640):
                 2,
             )
 
-        # Display the frame in a clean window
-        cv2.imshow(window_name, display_frame)
-
-        # Write the frame to output video
-        out.write(display_frame)
-
-        # Update progress bar
-        progress_bar.update(1)
-
-        # Toggle provider display on 'p' key press
-        key = cv2.waitKey(1) & 0xFF
-        if key == ord("q"):
-            print("\nProcessing interrupted by user")
-            break
-        elif key == ord("p"):
-            show_provider = not show_provider
-
-    progress_bar.close()
-    cap.release()
-    out.release()
-    cv2.destroyAllWindows()
-    print("Video processing complete. Result saved as 'onnx_result.mp4'.")
-
-
-def process_webcam(sess, device_id=0, class_names=None, input_size=640):
-    cap = cv2.VideoCapture(device_id)
-
-    if not cap.isOpened():
-        print(f"Error: Could not open webcam device {device_id}")
-        return
-
-    print(f"Webcam opened successfully. Press 'q' to quit.")
-
-    # Variables for FPS calculation
-    prev_time = 0
-    curr_time = 0
-    fps = 0
-
-    show_provider = True
-    provider = sess.get_providers()[0]
-
-    # Get input name from model metadata
-    input_name = sess.get_inputs()[0].name
-    is_preprocessed = input_name == "input_bgr"
-
-    while True:
-        # Calculate FPS
-        curr_time = time.time()
-        if curr_time - prev_time > 0:  # Avoid division by zero
-            fps = 1 / (curr_time - prev_time)
-        prev_time = curr_time
-
-        ret, frame = cap.read()
-        if not ret:
-            print("Error: Failed to capture frame from webcam")
-            break
-
-        # Convert frame to PIL image
-        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-        # Resize frame while preserving aspect ratio
-        resized_frame_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(
-            frame_pil, input_size
-        )
-        orig_size = np.array(
-            [[resized_frame_pil.size[1], resized_frame_pil.size[0]]], dtype=np.int64
-        )
-
-        if is_preprocessed:
-            # For models with preprocessing, input should be BGR float32 format
-            im_data = cv2.cvtColor(np.array(resized_frame_pil), cv2.COLOR_RGB2BGR)
-            im_data = im_data.astype(np.float32)  # Convert to float32
-            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
-        else:
-            # For models without preprocessing, normalize to 0-1 range
-            im_data = np.array(resized_frame_pil, dtype=np.float32) / 255.0
-            im_data = np.expand_dims(im_data.transpose(2, 0, 1), axis=0)
-
-        output = sess.run(
-            output_names=None,
-            input_feed={input_name: im_data, "orig_target_sizes": orig_size},
-        )
-
-        labels, boxes, scores = output
-
-        # Draw detections on the original frame
-        result_images = draw(
-            [frame_pil],
-            labels,
-            boxes,
-            scores,
-            [ratio],
-            [(pad_w, pad_h)],
-            class_names=class_names,
-        )
-        frame_with_detections = result_images[0]
-
-        # Convert back to OpenCV image for display
-        display_frame = cv2.cvtColor(np.array(frame_with_detections), cv2.COLOR_RGB2BGR)
-
-        # Add FPS text to the top right corner with dark blue background
-        fps_text = f"FPS: {fps:.1f}"
-        text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
-        text_x = display_frame.shape[1] - text_size[0] - 10
-        text_y = 30
-
-        # Draw background rectangle
-        cv2.rectangle(
-            display_frame,
-            (text_x - 5, text_y - text_size[1] - 5),
-            (text_x + text_size[0] + 5, text_y + 5),
-            (139, 0, 0),
-            -1,
-        )  # Dark blue background (BGR format)
-
-        # Draw text in white
-        cv2.putText(
-            display_frame,
-            fps_text,
-            (text_x, text_y),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.8,
-            (255, 255, 255),
-            2,
-        )
-
-        # Add provider text at the bottom center when show_provider is True
-        if show_provider:
-            provider_text = f"ONNX Runtime EP: {provider}"
+            # Add provider display
+            provider_text = f"Provider: {session.get_providers()[0]}"
             text_size = cv2.getTextSize(
                 provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
             )[0]
-            text_x = (display_frame.shape[1] - text_size[0]) // 2
-            text_y = display_frame.shape[0] - 20
+            text_x = (result_bgr.shape[1] - text_size[0]) // 2
+            text_y = result_bgr.shape[0] - 20
 
-            # Draw background rectangle
+            # Draw provider background rectangle
             cv2.rectangle(
-                display_frame,
+                result_bgr,
                 (text_x - 5, text_y - text_size[1] - 5),
                 (text_x + text_size[0] + 5, text_y + 5),
-                (0, 0, 255),
+                (139, 0, 0),
                 -1,
-            )  # Red background
+            )
 
-            # Draw text in white
+            # Draw provider text
             cv2.putText(
-                display_frame,
+                result_bgr,
                 provider_text,
                 (text_x, text_y),
                 cv2.FONT_HERSHEY_SIMPLEX,
@@ -451,25 +405,32 @@ def process_webcam(sess, device_id=0, class_names=None, input_size=640):
                 2,
             )
 
-        # Display the frame
-        cv2.imshow("Webcam Detection", display_frame)
-
-        # Toggle provider display on 'p' key press
-        key = cv2.waitKey(1) & 0xFF
-        if key == ord("q"):
-            break
-        elif key == ord("p"):
-            show_provider = not show_provider
-
-    cap.release()
-    cv2.destroyAllWindows()
-    print("Webcam processing stopped.")
-
-
-def main(args):
-    if args.provider == "cpu":
+            # Display the result
+            cv2.imshow("Webcam Detection", result_bgr)
+
+            # Handle key presses
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord("q"):
+                break
+
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+def run_inference_video(
+    model_path,
+    video_path,
+    class_names_path=None,
+    provider="cpu",
+    threshold=0.3,
+    video_width=640,
+):
+    """Run object detection on a video file."""
+    # Set up providers (same as webcam function)
+    if provider == "cpu":
         providers = ["CPUExecutionProvider"]
-    elif args.provider == "cuda":
+    elif provider == "cuda":
         providers = [
             (
                 "CUDAExecutionProvider",
@@ -482,7 +443,7 @@ def main(args):
             ),
             "CPUExecutionProvider",
         ]
-    elif args.provider == "tensorrt":
+    elif provider == "tensorrt":
         providers = [
             (
                 "TensorrtExecutionProvider",
@@ -496,73 +457,206 @@ def main(args):
             "CPUExecutionProvider",
         ]
 
+    # Initialize model session
     try:
         print(f"Loading ONNX model with providers: {providers}...")
-        sess_options = ort.SessionOptions()
-
-        sess = ort.InferenceSession(
-            args.onnx, sess_options=sess_options, providers=providers
-        )
-        print(f"Using provider: {sess.get_providers()[0]}")
-
+        session = ort.InferenceSession(model_path, providers=providers)
+        print(f"Using provider: {session.get_providers()[0]}")
     except Exception as e:
         print(f"Error creating inference session with providers {providers}: {e}")
         print("Attempting to fall back to CPU execution...")
-        sess = ort.InferenceSession(args.onnx, providers=["CPUExecutionProvider"])
+        session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
 
-    # Load class names if provided
+    # Load class names
     class_names = None
-    if args.class_names:
+    if class_names_path:
         try:
-            with open(args.class_names, "r") as f:
+            with open(class_names_path, "r") as f:
                 class_names = [line.strip() for line in f.readlines()]
             print(f"Loaded {len(class_names)} class names")
         except Exception as e:
             print(f"Error loading class names: {e}")
 
-    # Get input size from args
-    input_size = args.input_size
+    # Open video file
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise RuntimeError(f"Failed to open video file: {video_path}")
 
-    if args.webcam:
-        # Process webcam feed
-        process_webcam(sess, args.device_id, class_names, input_size)
-    else:
-        input_path = args.input
-        try:
-            # Try to open the input as an image
-            im_pil = Image.open(input_path).convert("RGB")
-            process_image(sess, im_pil, class_names, input_size)
-        except IOError:
-            # Not an image, process as video
-            process_video(sess, input_path, class_names, input_size)
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Create video writer
+    output_path = "detection_output.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+    # Initialize FPS calculation
+    prev_time = time.time()
+    fps_display = 0
+    frame_count = 0
+
+    try:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frame_count += 1
+            if frame_count % 10 == 0:
+                progress = (frame_count / total_frames) * 100
+                print(f"Processing: {progress:.1f}% complete", end="\r")
+
+            # Calculate FPS
+            current_time = time.time()
+            if current_time - prev_time > 0:
+                fps_display = 1 / (current_time - prev_time)
+            prev_time = current_time
+
+            # Calculate scaling and padding using video_width parameter
+            height, width = frame.shape[:2]
+            scale = video_width / max(height, width)
+            new_height = int(height * scale)
+            new_width = int(width * scale)
+
+            # Calculate padding
+            y_offset = (video_width - new_height) // 2
+            x_offset = (video_width - new_width) // 2
+
+            # Create model input with padding using video_width
+            model_input = np.zeros((video_width, video_width, 3), dtype=np.uint8)
+            model_input[
+                y_offset : y_offset + new_height, x_offset : x_offset + new_width
+            ] = cv2.resize(frame, (new_width, new_height))
+
+            # Convert BGR to RGB for model input
+            image = cv2.cvtColor(model_input, cv2.COLOR_BGR2RGB)
+
+            # Prepare input data
+            im_data = np.ascontiguousarray(
+                image.transpose(2, 0, 1),
+                dtype=np.float32,
+            )
+            im_data = np.expand_dims(im_data, axis=0)
+            orig_size = np.array(
+                [[video_width, video_width]], dtype=np.int64
+            )  # Use padded size
+
+            # Run inference
+            input_name = session.get_inputs()[0].name
+            outputs = session.run(
+                output_names=None,
+                input_feed={input_name: im_data, "orig_target_sizes": orig_size},
+            )
+
+            # Process outputs
+            labels, boxes, scores = outputs
+
+            # Scale boxes from padded 640x640 to original frame size
+            boxes = boxes[0]  # Remove batch dimension
+            boxes[:, [0, 2]] = (boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+            boxes[:, [1, 3]] = (boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
+
+            # Draw bounding boxes on the original frame
+            result_image = draw_boxes(
+                frame,  # Use original frame
+                labels[0],
+                boxes,
+                scores[0],
+                1.0,  # No additional scaling needed
+                (0, 0),  # No additional padding needed
+                threshold=threshold,
+                class_names=class_names,
+            )
+
+            # Add FPS counter and provider info (matching webcam style)
+            fps_text = f"FPS: {fps_display:.1f}"
+            text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            text_x = result_image.shape[1] - text_size[0] - 10
+            text_y = 30
+
+            # Draw FPS background rectangle
+            cv2.rectangle(
+                result_image,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw FPS text
+            cv2.putText(
+                result_image,
+                fps_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
+
+            # Add provider display at bottom (matching webcam style)
+            provider_text = f"Provider: {session.get_providers()[0]}"
+            text_size = cv2.getTextSize(
+                provider_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
+            )[0]
+            text_x = (result_image.shape[1] - text_size[0]) // 2
+            text_y = result_image.shape[0] - 20
+
+            # Draw provider background rectangle
+            cv2.rectangle(
+                result_image,
+                (text_x - 5, text_y - text_size[1] - 5),
+                (text_x + text_size[0] + 5, text_y + 5),
+                (139, 0, 0),
+                -1,
+            )
+
+            # Draw provider text
+            cv2.putText(
+                result_image,
+                provider_text,
+                (text_x, text_y),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),
+                2,
+            )
+
+            # Write frame to output video
+            out.write(result_image)
+
+            # Display frame (optional)
+            cv2.imshow("Video Detection", result_image)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+
+    finally:
+        cap.release()
+        out.release()
+        cv2.destroyAllWindows()
+        print(f"\nVideo processing complete. Output saved to {output_path}")
 
 
 if __name__ == "__main__":
     import argparse
 
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--onnx", type=str, required=True, help="Path to the ONNX model file."
-    )
-    parser.add_argument(
-        "--input", type=str, help="Path to the input image or video file."
-    )
+    parser = argparse.ArgumentParser(description="Simple ONNX object detection")
     parser.add_argument(
-        "--webcam", action="store_true", help="Use webcam as input source"
+        "--model", type=str, required=True, help="Path to ONNX model file"
     )
+    parser.add_argument("--image", type=str, help="Path to input image (optional)")
+    parser.add_argument("--webcam", action="store_true", help="Use webcam input")
     parser.add_argument(
-        "--device-id", type=int, default=0, help="Webcam device ID (default: 0)"
-    )
-    parser.add_argument(
-        "--class-names",
-        type=str,
-        help="Path to a text file with class names (one per line)",
-    )
-    parser.add_argument(
-        "--input-size",
+        "--video-width",
         type=int,
         default=640,
-        help="Input image size for the model (default: 640)",
+        help="Width of the video input in pixels (default: 640). Height will be adjusted to maintain aspect ratio",
+    )
+    parser.add_argument(
+        "--classes", type=str, help="Path to class names file (optional)"
     )
     parser.add_argument(
         "--provider",
@@ -571,9 +665,32 @@ def main(args):
         default="cpu",
         help="ONNXRuntime provider to use for inference",
     )
-    args = parser.parse_args()
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=0.3,
+        help="Detection confidence threshold (default: 0.3)",
+    )
+    parser.add_argument("--video", type=str, help="Path to input video file (optional)")
 
-    if not args.webcam and not args.input:
-        parser.error("Either --input or --webcam must be specified")
+    args = parser.parse_args()
 
-    main(args)
+    if args.webcam:
+        run_inference_webcam(
+            args.model, args.classes, args.provider, args.threshold, args.video_width
+        )
+    elif args.video:
+        run_inference_video(
+            args.model,
+            args.video,
+            args.classes,
+            args.provider,
+            args.threshold,
+            args.video_width,
+        )
+    elif args.image:
+        run_inference(
+            args.model, args.image, args.classes, args.threshold, args.provider
+        )
+    else:
+        parser.error("Either --image, --video, or --webcam must be specified")

From 1da7f63e0a2de1eb73ca08f0a708313046dcaa6e Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 16:21:48 +0800
Subject: [PATCH 13/41] update gradio demo

---
 scripts/gradio_demo.py | 290 ++++++++++++++++++++++-------------------
 1 file changed, 156 insertions(+), 134 deletions(-)

diff --git a/scripts/gradio_demo.py b/scripts/gradio_demo.py
index 36c61804..77c3fb8c 100644
--- a/scripts/gradio_demo.py
+++ b/scripts/gradio_demo.py
@@ -1,11 +1,14 @@
 import colorsys
 import os
+import argparse
+import time
 
 import gradio as gr
 import numpy as np
 import onnxruntime as ort
 import pandas as pd
 from PIL import Image, ImageDraw
+import cv2
 
 
 # Use absolute paths instead of relative paths
@@ -13,20 +16,6 @@
 MODEL_PATH = os.path.join(BASE_DIR, "models/deim-blood-cell-detection_nano.onnx")
 CLASS_NAMES_PATH = os.path.join(BASE_DIR, "models/classes.txt")
 
-def resize_with_aspect_ratio(image, size, interpolation=Image.BILINEAR):
-    """Resizes an image while maintaining aspect ratio and pads it."""
-    original_width, original_height = image.size
-    ratio = min(size / original_width, size / original_height)
-    new_width = int(original_width * ratio)
-    new_height = int(original_height * ratio)
-    image = image.resize((new_width, new_height), interpolation)
-
-    # Create a new image with the desired size and paste the resized image onto it
-    new_image = Image.new("RGB", (size, size))
-    new_image.paste(image, ((size - new_width) // 2, (size - new_height) // 2))
-    return new_image, ratio, (size - new_width) // 2, (size - new_height) // 2
-
-
 def generate_colors(num_classes):
     """Generate a list of distinct colors for different classes."""
     # Generate evenly spaced hues
@@ -42,76 +31,78 @@ def generate_colors(num_classes):
     return colors
 
 
-def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4, class_names=None):
+def draw(images, labels, boxes, scores, scales, paddings, thrh=0.4, class_names=None):
+    """Draw detection boxes on images."""
     result_images = []
-
-    # Generate colors for classes
-    num_classes = (
-        len(class_names) if class_names else 91
-    )  # Use length of class_names if available, otherwise default to COCO's 91 classes
-    colors = generate_colors(num_classes)
+    colors = generate_colors(len(class_names) if class_names else 91)
 
     for i, im in enumerate(images):
-        draw = ImageDraw.Draw(im)
-        scr = scores[i]
-        
-        # Get indices of scores above threshold
-        valid_indices = np.where(scr > thrh)[0]
-        
-        # Filter using these indices
+        # Convert PIL to numpy if needed
+        if isinstance(im, Image.Image):
+            im = np.array(im)
+
+        # Filter detections by threshold
+        valid_indices = scores[i] > thrh
         valid_labels = labels[i][valid_indices]
         valid_boxes = boxes[i][valid_indices]
-        valid_scores = scr[valid_indices]
-
-        for j, (lbl, bb, score) in enumerate(zip(valid_labels, valid_boxes, valid_scores)):
-            # Get color for this class
-            class_idx = int(lbl)
-            color = colors[class_idx % len(colors)]
+        valid_scores = scores[i][valid_indices]
 
-            # Convert RGB to hex for PIL
-            hex_color = "#{:02x}{:02x}{:02x}".format(*color)
+        # Scale boxes from padded size to original image size
+        scale = scales[i]
+        x_offset, y_offset = paddings[i]
+        
+        valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - x_offset) / scale  # x coordinates
+        valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - y_offset) / scale  # y coordinates
 
-            ratio = ratios[i]
-            pad_w, pad_h = paddings[i]
+        # Draw boxes
+        for label, box, score in zip(valid_labels, valid_boxes, valid_scores):
+            class_idx = int(label)
+            color = colors[class_idx % len(colors)]
             
-            # Adjust bounding boxes according to the resizing and padding
-            bb = [
-                (bb[0] - pad_w) / ratio,
-                (bb[1] - pad_h) / ratio,
-                (bb[2] - pad_w) / ratio,
-                (bb[3] - pad_h) / ratio,
-            ]
-
-            # Draw rectangle with class-specific color
-            draw.rectangle(bb, outline=hex_color, width=3)
-
-            # Use class name if available, otherwise use class index
+            # Convert coordinates to integers
+            box = [int(coord) for coord in box]
+            
+            # Draw rectangle
+            cv2.rectangle(im, (box[0], box[1]), (box[2], box[3]), color, 2)
+
+            # Prepare label text
             if class_names and class_idx < len(class_names):
                 label_text = f"{class_names[class_idx]} {score:.2f}"
             else:
                 label_text = f"Class {class_idx} {score:.2f}"
 
+            # Get text size
+            (text_width, text_height), _ = cv2.getTextSize(
+                label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
+            )
+
             # Draw text background
-            text_size = draw.textbbox((0, 0), label_text, font=None)
-            text_width = text_size[2] - text_size[0]
-            text_height = text_size[3] - text_size[1]
-
-            # Draw text background rectangle
-            draw.rectangle(
-                [bb[0], bb[1] - text_height - 4, bb[0] + text_width + 4, bb[1]],
-                fill=hex_color,
+            cv2.rectangle(
+                im,
+                (box[0], box[1] - text_height - 4),
+                (box[0] + text_width + 4, box[1]),
+                color,
+                -1,
             )
 
-            # Draw text in white or black depending on color brightness
-            brightness = (color[0] * 299 + color[1] * 587 + color[2] * 114) / 1000
-            text_color = "black" if brightness > 128 else "white"
+            # Calculate text color based on background brightness
+            brightness = sum(color) / 3
+            text_color = (0, 0, 0) if brightness > 128 else (255, 255, 255)
 
             # Draw text
-            draw.text(
-                (bb[0] + 2, bb[1] - text_height - 2), text=label_text, fill=text_color
+            cv2.putText(
+                im,
+                label_text,
+                (box[0] + 2, box[1] - 2),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.7,
+                text_color,
+                2,
             )
 
-        result_images.append(im)
+        # Convert back to PIL Image
+        result_images.append(Image.fromarray(im))
+
     return result_images
 
 
@@ -186,58 +177,67 @@ def load_class_names(class_names_path):
 
 def prepare_image(image, target_size=640):
     """
-    Prepare image for inference by converting to PIL and resizing.
+    Prepare image for inference by converting to PIL and resizing with padding.
 
     Args:
         image: Input image (PIL or numpy array)
         target_size: Target size for resizing (default: 640)
 
     Returns:
-        tuple: (resized_image, original_image, ratio, padding)
+        tuple: (model_input, original_image, scale, padding)
     """
-    # Convert to PIL image if needed
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image).convert("RGB")
+    # Convert to numpy array if PIL Image
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+    
+    # Calculate scaling and padding
+    height, width = image.shape[:2]
+    scale = target_size / max(height, width)
+    new_height = int(height * scale)
+    new_width = int(width * scale)
+
+    # Calculate padding
+    y_offset = (target_size - new_height) // 2
+    x_offset = (target_size - new_width) // 2
 
-    # Resize image while preserving aspect ratio
-    resized_image, ratio, pad_w, pad_h = resize_with_aspect_ratio(image, target_size)
+    # Create model input with padding
+    model_input = np.zeros((target_size, target_size, 3), dtype=np.uint8)
+    model_input[y_offset:y_offset + new_height, x_offset:x_offset + new_width] = cv2.resize(image, (new_width, new_height))
 
-    return resized_image, image, ratio, (pad_w, pad_h)
+    return model_input, image, scale, (x_offset, y_offset)
 
 
-def run_inference(session, image):
+def run_inference(session, image, target_size=640):
     """
     Run inference on the prepared image.
 
     Args:
         session: ONNX runtime session
-        image: Prepared PIL image
+        image: Prepared image array
+        target_size: Target size used for padding
 
     Returns:
         tuple: (labels, boxes, scores)
     """
-    # Check if image is None
-    if image is None:
-        raise ValueError("Input image is None")
-        
-    # Get original image dimensions
-    orig_height, orig_width = image.size[1], image.size[0]
-    # Convert to int64 as expected by the model
-    orig_size = np.array([[orig_height, orig_width]], dtype=np.int64)
-
-    # Convert PIL image to numpy array and normalize to 0-1 range
-    im_data = np.array(image, dtype=np.float32) / 255.0
-    # Transpose from HWC to CHW format
-    im_data = im_data.transpose(2, 0, 1)
-    # Add batch dimension
-    im_data = np.expand_dims(im_data, axis=0)
-
-    output = session.run(
+    # Convert BGR to RGB for model input
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Prepare input data
+    im_data = np.ascontiguousarray(
+        image_rgb.transpose(2, 0, 1),  # HWC to CHW format
+        dtype=np.float32,
+    )
+    im_data = np.expand_dims(im_data, axis=0)  # Add batch dimension
+    orig_size = np.array([[target_size, target_size]], dtype=np.int64)  # Use padded size
+
+    # Get input name and run inference
+    input_name = session.get_inputs()[0].name
+    outputs = session.run(
         output_names=None,
-        input_feed={"images": im_data, "orig_target_sizes": orig_size},
+        input_feed={input_name: im_data, "orig_target_sizes": orig_size},
     )
 
-    return output  # labels, boxes, scores
+    return outputs
 
 
 def count_objects(labels, scores, confidence_threshold, class_names):
@@ -310,69 +310,63 @@ def create_bar_data(object_counts):
 
 
 def predict(image, model_path, class_names_path, confidence_threshold, image_size):
-    """
-    Main prediction function that orchestrates the detection pipeline.
-
-    Args:
-        image: Input image
-        model_path: Path to ONNX model
-        class_names_path: Path to class names file or list of class names
-        confidence_threshold: Detection confidence threshold
-        image_size: Size to resize the image to before inference
-
-    Returns:
-        tuple: (result_image, status_message, bar_data)
-    """
-    # Check if image is None
+    """Main prediction function."""
     if image is None:
         return None, "Error: No image provided", None
-        
+
     # Load model
+    model_load_start = time.time()
     session, error = load_model(model_path)
+    model_load_time = time.time() - model_load_start
+    
     if error:
         return None, error, None
 
     # Load class names
     class_names = load_class_names(class_names_path)
-    
-    # Debug print to verify class names are loaded correctly
-    print(f"Class names for detection: {class_names}")
 
     try:
-        # Prepare image with the selected size
-        resized_image, original_image, ratio, padding = prepare_image(image, image_size)
+        # Prepare image
+        preprocess_start = time.time()
+        model_input, original_image, scale, padding = prepare_image(image, image_size)
+        preprocess_time = time.time() - preprocess_start
 
         # Run inference
-        output = run_inference(session, resized_image)
+        inference_start = time.time()
+        outputs = run_inference(session, model_input, image_size)
+        inference_time = time.time() - inference_start
         
-        # Check if output is valid
-        if not output or len(output) < 3:
+        if not outputs or len(outputs) < 3:
             return None, "Error: Model output is invalid", None
             
-        labels, boxes, scores = output
+        labels, boxes, scores = outputs
 
-        # Draw detections on the original image
+        # Draw detections
+        postprocess_start = time.time()
         result_images = draw(
             [original_image],
             labels,
             boxes,
             scores,
-            [ratio],
+            [scale],
             [padding],
             thrh=confidence_threshold,
             class_names=class_names,
         )
 
-        # Count objects by class
+        # Count objects and create visualizations
         object_counts = count_objects(labels, scores, confidence_threshold, class_names)
-        
-        # Debug print to verify object counts
-        print(f"Object counts: {object_counts}")
+        postprocess_time = time.time() - postprocess_start
 
-        # Create status message
+        # Create status message with timing information
         status_message = create_status_message(object_counts)
-
-        # Create bar plot data
+        status_message += "\n\nLatency Information:"
+        status_message += f"\n- Model Loading: {model_load_time*1000:.1f}ms"
+        status_message += f"\n- Preprocessing: {preprocess_time*1000:.1f}ms"
+        status_message += f"\n- Inference: {inference_time*1000:.1f}ms"
+        status_message += f"\n- Postprocessing: {postprocess_time*1000:.1f}ms"
+        status_message += f"\n- Total Time: {(model_load_time + preprocess_time + inference_time + postprocess_time)*1000:.1f}ms"
+        
         bar_data = create_bar_data(object_counts)
 
         return result_images[0], status_message, bar_data
@@ -400,7 +394,7 @@ def build_interface(model_path, class_names_path, example_images=None):
         gr.Markdown("Configure the model and run inference on an image.")
         
         # Add model selection
-        with gr.Accordion("Model Settings", open=True):
+        with gr.Accordion("Model Settings", open=False):
             with gr.Row():
                 custom_model_path = gr.File(
                     label="Custom Model File (ONNX)",
@@ -426,11 +420,13 @@ def build_interface(model_path, class_names_path, example_images=None):
                         label="Confidence Threshold",
                     )
                     
-                    image_size = gr.Dropdown(
-                        choices=[320, 480, 640, 800],
+                    image_size = gr.Slider(
+                        minimum=32,
+                        maximum=1920,
                         value=640,
+                        step=32,
                         label="Image Size",
-                        info="Select image size for inference"
+                        info="Select image size for inference (larger = slower but potentially more accurate)"
                     )
                 
                 submit_btn = gr.Button("Run Inference", variant="primary")
@@ -495,12 +491,38 @@ def get_classes_path(custom_file, default_path):
         return demo
 
 
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description='DEIMKit Detection Demo')
+    parser.add_argument(
+        '--model', 
+        type=str,
+        default=MODEL_PATH,
+        help='Path to ONNX model file'
+    )
+    parser.add_argument(
+        '--classes', 
+        type=str,
+        default=CLASS_NAMES_PATH,
+        help='Path to class names file'
+    )
+    parser.add_argument(
+        '--examples',
+        type=str,
+        default=os.path.join(BASE_DIR, "examples"),
+        help='Path to directory containing example images'
+    )
+    return parser.parse_args()
+
+
 def launch_demo():
     """
-    Launch the Gradio demo with hardcoded model and class names paths.
+    Launch the Gradio demo with model and class names paths from command line arguments.
     """
+    args = parse_args()
+    
     # Create examples directory if it doesn't exist
-    examples_dir = os.path.join(BASE_DIR, "examples")
+    examples_dir = args.examples
     if not os.path.exists(examples_dir):
         os.makedirs(examples_dir)
         print(f"Created examples directory at {examples_dir}")
@@ -513,9 +535,9 @@ def launch_demo():
             for f in os.listdir(examples_dir) 
             if f.lower().endswith(('.png', '.jpg', '.jpeg'))
         ]
-        print(f"Found {len(example_images)} example images")
+        print(f"Found {len(example_images)} example images in {examples_dir}")
     
-    demo = build_interface(MODEL_PATH, CLASS_NAMES_PATH, example_images)
+    demo = build_interface(args.model, args.classes, example_images)
     
     # Launch the demo without the examples parameter
     demo.launch(share=False, inbrowser=True)  # Set share=True if you want to create a shareable link

From a1a605d0a45203c5fb1fae6f5ac91bee97942799 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 16:23:32 +0800
Subject: [PATCH 14/41] update readme

---
 README.md              |   2 +-
 assets/gradio_demo.png | Bin 941843 -> 937174 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e231866f..3af0daf2 100644
--- a/README.md
+++ b/README.md
@@ -345,7 +345,7 @@ pixi run -e cpu live-inference --onnx model.onnx --input video.mp4 --class-names
 
 Launch Gradio app
 ```bash
-pixi run -e cuda gradio-demo
+pixi run gradio-demo --model "best_prep.onnx" --classes "classes.txt" --examples "Rock Paper Scissors SXSW.v14i.coco/test"
 ```
 
 ```bash
diff --git a/assets/gradio_demo.png b/assets/gradio_demo.png
index de1b4033d775a9872225c195b2549a7ec1d1bbbb..6e2cdaa6c90f84ab80889b524cd7793fc927afc2 100644
GIT binary patch
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From f3c6ded225305440181808ede2e94e16fd45c7e1 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 16:27:45 +0800
Subject: [PATCH 15/41] update command

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3af0daf2..7cbbecd3 100644
--- a/README.md
+++ b/README.md
@@ -239,7 +239,7 @@ output_path = exporter.to_onnx(
 Run a Gradio app to interact with your model.
 
 ```bash
-python scripts/gradio_demo.py
+python scripts/gradio_demo.py --model "best_prep.onnx" --classes "classes.txt" --examples "Rock Paper Scissors SXSW.v14i.coco/test"
 ```
 ![alt text](assets/gradio_demo.png)
 

From 298868ff0685efede7185f51a2d84e69d6a8aba6 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 21:56:17 +0800
Subject: [PATCH 16/41] update readme

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7cbbecd3..ae095def 100644
--- a/README.md
+++ b/README.md
@@ -239,7 +239,10 @@ output_path = exporter.to_onnx(
 Run a Gradio app to interact with your model.
 
 ```bash
-python scripts/gradio_demo.py --model "best_prep.onnx" --classes "classes.txt" --examples "Rock Paper Scissors SXSW.v14i.coco/test"
+python scripts/gradio_demo.py \
+    --model "best.onnx" \
+    --classes "classes.txt" \
+    --examples "Rock Paper Scissors SXSW.v14i.coco/test"
 ```
 ![alt text](assets/gradio_demo.png)
 

From 33c480c5e27193a622a20f844f5831d2641ff8ad Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 22:02:38 +0800
Subject: [PATCH 17/41] update

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index ae095def..e1b9b500 100644
--- a/README.md
+++ b/README.md
@@ -246,6 +246,11 @@ python scripts/gradio_demo.py \
 ```
 ![alt text](assets/gradio_demo.png)
 
+> [!TIP]
+> The demo app uses onnx model and onnxruntime for inference. Additionally, I have also made it that the ONNX model to accept any input size, despite the original model was trained on 640x640 images. 
+> This means you can use any image size you want. Play around with the input size to see what works best for your model. 
+> Some objects are visible even at lower input sizes, this means you can use a lower input size to speed up inference.
+
 ### Live Inference
 Run live inference on a video, image or webcam using ONNXRuntime. This runs on CPU by default.
 If you would like to use the CUDA backend, you can install the `onnxruntime-gpu` package and uninstall the `onnxruntime` package.

From 3e5f9df00b862520c87bfbd7c533f450d2778ad9 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 22:03:27 +0800
Subject: [PATCH 18/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e1b9b500..e8a2e24a 100644
--- a/README.md
+++ b/README.md
@@ -248,7 +248,7 @@ python scripts/gradio_demo.py \
 
 > [!TIP]
 > The demo app uses onnx model and onnxruntime for inference. Additionally, I have also made it that the ONNX model to accept any input size, despite the original model was trained on 640x640 images. 
-> This means you can use any image size you want. Play around with the input size to see what works best for your model. 
+> This means you can use any image size you want. Play around with the input size slider to see what works best for your model. 
 > Some objects are visible even at lower input sizes, this means you can use a lower input size to speed up inference.
 
 ### Live Inference

From 75bf332f89fedd612b469c4be01356e06043ccda Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 22:03:52 +0800
Subject: [PATCH 19/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e8a2e24a..949fb256 100644
--- a/README.md
+++ b/README.md
@@ -246,7 +246,7 @@ python scripts/gradio_demo.py \
 ```
 ![alt text](assets/gradio_demo.png)
 
-> [!TIP]
+> [!NOTE]
 > The demo app uses onnx model and onnxruntime for inference. Additionally, I have also made it that the ONNX model to accept any input size, despite the original model was trained on 640x640 images. 
 > This means you can use any image size you want. Play around with the input size slider to see what works best for your model. 
 > Some objects are visible even at lower input sizes, this means you can use a lower input size to speed up inference.

From 94184c8c8e6dd7608ff9910b0462ddea89de8d8c Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Mon, 31 Mar 2025 22:28:26 +0800
Subject: [PATCH 20/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 949fb256..b56ac856 100644
--- a/README.md
+++ b/README.md
@@ -260,7 +260,7 @@ For video inference, specify the path to the video file as the input. Output vid
 ```bash
 python scripts/live_inference.py \
     --model model.onnx           # Path to the ONNX model file
-    --input video.mp4           # Path to the input video file
+    --video video.mp4           # Path to the input video file
     --classes classes.txt       # Path to the classes file with each name on a new row
     --video-width 320          # Input size for the model
     --provider cpu             # Execution provider (cpu/cuda/tensorrt)

From 2453ee5a2665f57fbf65a7ba36dae57939ca9f45 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 11:54:45 +0800
Subject: [PATCH 21/41] Refine README instructions for live inference commands,
 ensuring consistent formatting and clarifying input parameters for video and
 image inference.

---
 README.md | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index b56ac856..7a4929e2 100644
--- a/README.md
+++ b/README.md
@@ -258,36 +258,36 @@ If you would like to use the CUDA backend, you can install the `onnxruntime-gpu`
 For video inference, specify the path to the video file as the input. Output video will be saved as `onnx_result.mp4` in the current directory.
 
 ```bash
-python scripts/live_inference.py \
+python scripts/live_inference.py 
     --model model.onnx           # Path to the ONNX model file
-    --video video.mp4           # Path to the input video file
-    --classes classes.txt       # Path to the classes file with each name on a new row
-    --video-width 320          # Input size for the model
-    --provider cpu             # Execution provider (cpu/cuda/tensorrt)
-    --threshold 0.3           # Detection confidence threshold
+    --video video.mp4            # Path to the input video file
+    --classes classes.txt        # Path to the classes file with each name on a new row
+    --video-width 320            # Input size for the model
+    --provider cpu               # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3              # Detection confidence threshold
 ```
 
 You can also run live inference on a webcam by setting the `--webcam` flag.
 
 ```bash
-python scripts/live_inference.py \
+python scripts/live_inference.py 
     --model model.onnx          # Path to the ONNX model file
     --webcam                    # Use webcam as input source
-    --classes classes.txt       # Path to the classes file. Each class name should be on a new line
-    --video-width 720          # Input size for the model
-    --provider tensorrt        # Execution provider (cpu/cuda/tensorrt)
-    --threshold 0.3           # Detection confidence threshold
+    --classes classes.txt       # Path to the classes file with each name on a new row
+    --video-width 720           # Input size for the model
+    --provider tensorrt         # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3             # Detection confidence threshold
 ```
 
 For image inference, specify the path to the image file as the input.
 
 ```bash
-python scripts/live_inference.py \
+python scripts/live_inference.py 
     --model model.onnx          # Path to the ONNX model file
-    --input image.jpg           # Path to the input image file
-    --classes classes.txt       # Path to the classes file. Each class name should be on a new line
-    --provider cpu             # Execution provider (cpu/cuda/tensorrt)
-    --threshold 0.3           # Detection confidence threshold
+    --image image.jpg           # Path to the input image file
+    --classes classes.txt       # Path to the classes file with each name on a new row
+    --provider cpu              # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3             # Detection confidence threshold
 ```
 
 The following is a demo of image inference

From 34be46967536fa1019d56160723fd555a07c8afe Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 14:22:10 +0800
Subject: [PATCH 22/41] update

---
 README.md                 |   5 +
 assets/exported_onnx.png  | Bin 0 -> 54816 bytes
 pixi.lock                 | 338 +++++++++++++++++++-------------------
 scripts/live_inference.py |  56 ++++++-
 src/deimkit/exporter.py   |   2 +-
 5 files changed, 231 insertions(+), 170 deletions(-)
 create mode 100644 assets/exported_onnx.png

diff --git a/README.md b/README.md
index 7a4929e2..d7c7a231 100644
--- a/README.md
+++ b/README.md
@@ -235,6 +235,11 @@ output_path = exporter.to_onnx(
 )
 ```
 
+> [!NOTE]
+> The exported model will accept raw BGR images of any size. It will also handle the preprocessing internally.
+> 
+> ![onnx model](assets/exported_onnx.png)
+
 ### Gradio App
 Run a Gradio app to interact with your model.
 
diff --git a/assets/exported_onnx.png b/assets/exported_onnx.png
new file mode 100644
index 0000000000000000000000000000000000000000..39d75e2ad14e681267e71bacff623ef34dd2684f
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.6-h537db12_0.conda
-  sha256: 77922d8dd2faf88ac6accaeebf06409d1820486fde710cff6b554d12273e46be
-  md5: 31d5107f75b2f204937728417e2e39e5
+  size: 39839
+  timestamp: 1743434670405
+- conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.6-h537db12_1.conda
+  sha256: d3b0b8812eab553d3464bbd68204f007f1ebadf96ce30eb0cbc5159f72e353f5
+  md5: 85d8fa5e55ed8f93f874b3b23ed54ec6
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -3049,8 +3054,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 40830
-  timestamp: 1739260917585
+  size: 44978
+  timestamp: 1743435053850
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h767d61c_2.conda
   sha256: 3a572d031cb86deb541d15c1875aaa097baefc0c580b54dc61f5edab99215792
   md5: ef504d1acbd74b7cc6849ef8af47dd03
@@ -3607,10 +3612,10 @@ packages:
   - sphinx ; extra == 'docs'
   - gmpy2>=2.1.0a4 ; platform_python_implementation != 'PyPy' and extra == 'gmpy'
   - pytest>=4.6 ; extra == 'tests'
-- pypi: https://files.pythonhosted.org/packages/e9/96/79a6168dc7a5098066e097c01a45d01608c8df6552dfb92a2676ce623186/narwhals-1.32.0-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/41/c1/e9bc6b67c774e7c1f939c91ea535f18f7644fedc61b20d6baa861ad52b34/narwhals-1.33.0-py3-none-any.whl
   name: narwhals
-  version: 1.32.0
-  sha256: 8bdbf3f76155887412eea04b0b06303856ac1aa3d9e8bda5b5e54612855fa560
+  version: 1.33.0
+  sha256: f653319112fd121a1f1c18a40cf70dada773cdacfd53e62c2aa0afae43c17129
   requires_dist:
   - cudf>=24.10.0 ; extra == 'cudf'
   - dask[dataframe]>=2024.8 ; extra == 'dask'
@@ -4333,19 +4338,20 @@ packages:
   - pkg:pypi/pickleshare?source=hash-mapping
   size: 11748
   timestamp: 1733327448200
-- pypi: https://files.pythonhosted.org/packages/3a/c6/fce9255272bcf0c39e15abd2f8fd8429a954cf344469eaceb9d0d1366913/pillow-11.1.0-cp311-cp311-win_amd64.whl
+- pypi: https://files.pythonhosted.org/packages/75/c9/2b0e8350b812825a43b53a1f49823e933514dbd2c7c6303daec7b28d7b57/pillow-11.2.0-cp311-cp311-win_amd64.whl
   name: pillow
-  version: 11.1.0
-  sha256: fbd43429d0d7ed6533b25fc993861b8fd512c42d04514a0dd6337fb3ccf22761
+  version: 11.2.0
+  sha256: 4d1c229f800addb9158c26661c6ade9cb038ff7bb98e3c0149e8fd3a7b6e6e08
   requires_dist:
   - furo ; extra == 'docs'
   - olefile ; extra == 'docs'
-  - sphinx>=8.1 ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
   - sphinx-copybutton ; extra == 'docs'
   - sphinx-inline-tabs ; extra == 'docs'
   - sphinxext-opengraph ; extra == 'docs'
   - olefile ; extra == 'fpx'
   - olefile ; extra == 'mic'
+  - pyarrow ; extra == 'test-arrow'
   - check-manifest ; extra == 'tests'
   - coverage>=7.4.2 ; extra == 'tests'
   - defusedxml ; extra == 'tests'
@@ -4360,19 +4366,20 @@ packages:
   - typing-extensions ; python_full_version < '3.10' and extra == 'typing'
   - defusedxml ; extra == 'xmp'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/48/a4/fbfe9d5581d7b111b28f1d8c2762dee92e9821bb209af9fa83c940e507a0/pillow-11.1.0-cp311-cp311-manylinux_2_28_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/90/c5/d4eaed054f75a3ffbd127190200b26c949acda13794aef6cf0ab91f2fb00/pillow-11.2.0-cp311-cp311-manylinux_2_28_x86_64.whl
   name: pillow
-  version: 11.1.0
-  sha256: 837060a8599b8f5d402e97197d4924f05a2e0d68756998345c829c33186217b1
+  version: 11.2.0
+  sha256: 92baf77b80de05ede82b14d92bf89a7f0067501b9476df0a162004f3bb015b50
   requires_dist:
   - furo ; extra == 'docs'
   - olefile ; extra == 'docs'
-  - sphinx>=8.1 ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
   - sphinx-copybutton ; extra == 'docs'
   - sphinx-inline-tabs ; extra == 'docs'
   - sphinxext-opengraph ; extra == 'docs'
   - olefile ; extra == 'fpx'
   - olefile ; extra == 'mic'
+  - pyarrow ; extra == 'test-arrow'
   - check-manifest ; extra == 'tests'
   - coverage>=7.4.2 ; extra == 'tests'
   - defusedxml ; extra == 'tests'
@@ -4387,19 +4394,20 @@ packages:
   - typing-extensions ; python_full_version < '3.10' and extra == 'typing'
   - defusedxml ; extra == 'xmp'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/d9/45/3fe487010dd9ce0a06adf9b8ff4f273cc0a44536e234b0fad3532a42c15b/pillow-11.1.0-cp311-cp311-macosx_11_0_arm64.whl
+- pypi: https://files.pythonhosted.org/packages/ea/75/947f47ed4667192ded5ae85336518068170c1450abf46da8a80bec84eb21/pillow-11.2.0-cp311-cp311-macosx_11_0_arm64.whl
   name: pillow
-  version: 11.1.0
-  sha256: 96f82000e12f23e4f29346e42702b6ed9a2f2fea34a740dd5ffffcc8c539eb35
+  version: 11.2.0
+  sha256: f0125a9e668df3e83ff2cfa5800dfe5646591449abae40abf75a1d8b325ddd33
   requires_dist:
   - furo ; extra == 'docs'
   - olefile ; extra == 'docs'
-  - sphinx>=8.1 ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
   - sphinx-copybutton ; extra == 'docs'
   - sphinx-inline-tabs ; extra == 'docs'
   - sphinxext-opengraph ; extra == 'docs'
   - olefile ; extra == 'fpx'
   - olefile ; extra == 'mic'
+  - pyarrow ; extra == 'test-arrow'
   - check-manifest ; extra == 'tests'
   - coverage>=7.4.2 ; extra == 'tests'
   - defusedxml ; extra == 'tests'
@@ -4703,10 +4711,10 @@ packages:
   version: 0.0.20
   sha256: 8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104
   requires_python: '>=3.8'
-- conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-5_cp311.conda
-  build_number: 5
-  sha256: 2660b8059b3ee854bc5d3c6b1fce946e5bd2fe8fbca7827de2c5885ead6209de
-  md5: 139a8d40c8a2f430df31048949e450de
+- conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-6_cp311.conda
+  build_number: 6
+  sha256: 2ff22fffe5bb93802c1687b5c4a34b9062394b78f23cfb5c1c1ef9b635bb030e
+  md5: 37ec65e056b9964529c0e1e2697b9955
   constrains:
   - python 3.11.* *_cpython
   arch: x86_64
@@ -4714,12 +4722,12 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 6211
-  timestamp: 1723823324668
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.11-5_cp311.conda
-  build_number: 5
-  sha256: adc05729b7e0aca7b436e60a86f10822a92185dfcb48d66d6444e3629d3a1f6a
-  md5: 3b855e3734344134cb56c410f729c340
+  size: 6853
+  timestamp: 1743483206119
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python_abi-3.11-6_cp311.conda
+  build_number: 6
+  sha256: 3d17ac9da54e92fd3d8b52a37aecdf532a45bbc2c0025871da78cec469f1aff7
+  md5: b30f805c0fccfebec5012f9f4c2ccfd9
   constrains:
   - python 3.11.* *_cpython
   arch: arm64
@@ -4727,12 +4735,12 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 6308
-  timestamp: 1723823096865
-- conda: https://conda.anaconda.org/conda-forge/win-64/python_abi-3.11-5_cp311.conda
-  build_number: 5
-  sha256: 9b210e5807dd9c9ed71ff192a95f1872da597ddd10e7cefec93a922fe22e598a
-  md5: 895b873644c11ccc0ab7dba2d8513ae6
+  size: 6983
+  timestamp: 1743483247301
+- conda: https://conda.anaconda.org/conda-forge/win-64/python_abi-3.11-6_cp311.conda
+  build_number: 6
+  sha256: 82b09808cc4f80212be7539d542d5853e0aaa593bc715f02b831c0ea0552b8bf
+  md5: 0cdb3079c532b4d216bc9efacd510138
   constrains:
   - python 3.11.* *_cpython
   arch: x86_64
@@ -4740,8 +4748,8 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 6707
-  timestamp: 1723823225752
+  size: 7348
+  timestamp: 1743483205320
 - pypi: https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl
   name: pytz
   version: '2025.2'
@@ -4912,10 +4920,10 @@ packages:
   purls: []
   size: 177240
   timestamp: 1728886815751
-- pypi: https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/0d/9b/63f4c7ebc259242c89b3acafdb37b41d1185c07ff0011164674e9076b491/rich-14.0.0-py3-none-any.whl
   name: rich
-  version: 13.9.4
-  sha256: 6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90
+  version: 14.0.0
+  sha256: 1c9491e1951aac09caffd42f448ee3d04e58923ffe14993f6e83068dc395d7e0
   requires_dist:
   - ipywidgets>=7.5.1,<9 ; extra == 'jupyter'
   - markdown-it-py>=2.2.0
diff --git a/scripts/live_inference.py b/scripts/live_inference.py
index 7addda79..1d2835dd 100644
--- a/scripts/live_inference.py
+++ b/scripts/live_inference.py
@@ -116,7 +116,7 @@ def run_inference(
             (
                 "TensorrtExecutionProvider",
                 {
-                    "trt_fp16_enable": False,
+                    "trt_fp16_enable": True,
                     "trt_engine_cache_enable": True,
                     "trt_engine_cache_path": "./trt_cache",
                     "trt_timing_cache_enable": True,
@@ -351,7 +351,31 @@ def run_inference_webcam(
             # No need to convert back to BGR since we're using the original frame
             result_bgr = result_image
 
-            # Add FPS and provider display
+            # Add video width display at top left with dark green background
+            width_text = f"Width: {int(actual_width)}px"
+            text_size = cv2.getTextSize(width_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            
+            # Draw dark green background rectangle
+            cv2.rectangle(
+                result_bgr,
+                (5, 5),  # Slight padding from corner
+                (text_size[0] + 15, 35),  # Add padding around text
+                (0, 100, 0),  # Dark green in BGR
+                -1,  # Filled rectangle
+            )
+
+            # Draw text
+            cv2.putText(
+                result_bgr,
+                width_text,
+                (10, 30),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),  # White text
+                2,
+            )
+
+            # Add FPS display (existing code)
             fps_text = f"FPS: {fps_display:.1f}"
             text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
             text_x = result_bgr.shape[1] - text_size[0] - 10
@@ -390,7 +414,7 @@ def run_inference_webcam(
                 result_bgr,
                 (text_x - 5, text_y - text_size[1] - 5),
                 (text_x + text_size[0] + 5, text_y + 5),
-                (139, 0, 0),
+                (0, 0, 139),
                 -1,
             )
 
@@ -571,7 +595,31 @@ def run_inference_video(
                 class_names=class_names,
             )
 
-            # Add FPS counter and provider info (matching webcam style)
+            # Add video width display at top left with dark green background
+            width_text = f"Width: {frame_width}px"
+            text_size = cv2.getTextSize(width_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+            
+            # Draw dark green background rectangle
+            cv2.rectangle(
+                result_image,
+                (5, 5),  # Slight padding from corner
+                (text_size[0] + 15, 35),  # Add padding around text
+                (0, 100, 0),  # Dark green in BGR
+                -1,  # Filled rectangle
+            )
+
+            # Draw text
+            cv2.putText(
+                result_image,
+                width_text,
+                (10, 30),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (255, 255, 255),  # White text
+                2,
+            )
+
+            # Add FPS counter and provider info (existing code)
             fps_text = f"FPS: {fps_display:.1f}"
             text_size = cv2.getTextSize(fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
             text_x = result_image.shape[1] - text_size[0] - 10
diff --git a/src/deimkit/exporter.py b/src/deimkit/exporter.py
index f4b27ef0..8a4fdb63 100644
--- a/src/deimkit/exporter.py
+++ b/src/deimkit/exporter.py
@@ -77,7 +77,7 @@ def to_onnx(
         simplify: bool = True,
         dynamic_batch: bool = True,
         dynamic_input_size: bool = True,
-        include_preprocessing: bool = False,
+        include_preprocessing: bool = True,
         fp16: bool = False,
         opset_version: int = 20,
         device: Optional[str] = None,

From 1bd403d448cd36e85a9ea37e9c73a83f9ed2af92 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 14:23:08 +0800
Subject: [PATCH 23/41] udpate

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d7c7a231..78062591 100644
--- a/README.md
+++ b/README.md
@@ -241,7 +241,7 @@ output_path = exporter.to_onnx(
 > ![onnx model](assets/exported_onnx.png)
 
 ### Gradio App
-Run a Gradio app to interact with your model.
+Run a Gradio app to interact with your model. The app will accept raw BGR images of any size. It will also handle the preprocessing internally using the exported ONNX model.
 
 ```bash
 python scripts/gradio_demo.py \

From f1e3cddce85103ba5243b8313ede8b61560ee675 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 15:39:14 +0800
Subject: [PATCH 24/41] update

---
 README.md | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 78062591..5ce77fc3 100644
--- a/README.md
+++ b/README.md
@@ -260,6 +260,20 @@ python scripts/gradio_demo.py \
 Run live inference on a video, image or webcam using ONNXRuntime. This runs on CPU by default.
 If you would like to use the CUDA backend, you can install the `onnxruntime-gpu` package and uninstall the `onnxruntime` package.
 
+For running inference on a webcam, set the `--webcam` flag.
+
+```bash
+python scripts/live_inference.py 
+    --model model.onnx          # Path to the ONNX model file
+    --webcam                    # Use webcam as input source
+    --classes classes.txt       # Path to the classes file with each name on a new row
+    --video-width 720           # Input size for the model
+    --provider tensorrt         # Execution provider (cpu/cuda/tensorrt)
+    --threshold 0.3             # Detection confidence threshold
+```
+
+
+
 For video inference, specify the path to the video file as the input. Output video will be saved as `onnx_result.mp4` in the current directory.
 
 ```bash
@@ -272,18 +286,6 @@ python scripts/live_inference.py
     --threshold 0.3              # Detection confidence threshold
 ```
 
-You can also run live inference on a webcam by setting the `--webcam` flag.
-
-```bash
-python scripts/live_inference.py 
-    --model model.onnx          # Path to the ONNX model file
-    --webcam                    # Use webcam as input source
-    --classes classes.txt       # Path to the classes file with each name on a new row
-    --video-width 720           # Input size for the model
-    --provider tensorrt         # Execution provider (cpu/cuda/tensorrt)
-    --threshold 0.3             # Detection confidence threshold
-```
-
 For image inference, specify the path to the image file as the input.
 
 ```bash

From 2708b880afc28a91c0cccc8875eeeedea806c215 Mon Sep 17 00:00:00 2001
From: Dickson Neoh <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 15:40:41 +0800
Subject: [PATCH 25/41] Update README.md

---
 README.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/README.md b/README.md
index 5ce77fc3..54ca04c8 100644
--- a/README.md
+++ b/README.md
@@ -273,6 +273,26 @@ python scripts/live_inference.py
 ```
 
 
+https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
+
+
+
+https://github.com/user-attachments/assets/31a8644e-e0c6-4bba-9d4f-857a3d0b53e1
+
+
+
+https://github.com/user-attachments/assets/aa267f05-5dbd-4824-973c-62f3b8f59c80
+
+
+
+https://github.com/user-attachments/assets/3d0c04c0-645a-4d54-86c0-991930491113
+
+
+
+https://github.com/user-attachments/assets/f4afff9c-3e6d-4965-ab86-0d4de7ce1a44
+
+
+
 
 For video inference, specify the path to the video file as the input. Output video will be saved as `onnx_result.mp4` in the current directory.
 

From 50727ee64501ae8513b1c6c39c0dfa29d473542a Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 15:43:48 +0800
Subject: [PATCH 26/41] update

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 54ca04c8..2ef8fcdb 100644
--- a/README.md
+++ b/README.md
@@ -272,6 +272,7 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths.
 
 https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
 

From 4926ec9b7bd0be0ccfb95e6b5b7b30b452ffd7b9 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 15:44:53 +0800
Subject: [PATCH 27/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2ef8fcdb..6c624a89 100644
--- a/README.md
+++ b/README.md
@@ -272,7 +272,7 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
-Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths.
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images.
 
 https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
 

From a3951b6ee259127870d171836e13dca2d72e390e Mon Sep 17 00:00:00 2001
From: Dickson Neoh <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 15:56:37 +0800
Subject: [PATCH 28/41] Update README.md

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 6c624a89..d76c8704 100644
--- a/README.md
+++ b/README.md
@@ -306,6 +306,8 @@ python scripts/live_inference.py
     --provider cpu               # Execution provider (cpu/cuda/tensorrt)
     --threshold 0.3              # Detection confidence threshold
 ```
+https://github.com/user-attachments/assets/6bc1dc6a-a223-4220-954d-2dab5c75b4a8
+
 
 For image inference, specify the path to the image file as the input.
 
@@ -318,6 +320,9 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
+
+
+
 The following is a demo of image inference
 
 ![image](assets/sample_result_image_1.jpg)

From bda06ddafb53889b81d3ddc625725875de8b84cb Mon Sep 17 00:00:00 2001
From: Dickson Neoh <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 17:12:31 +0800
Subject: [PATCH 29/41] Update README.md

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index d76c8704..e8878f1b 100644
--- a/README.md
+++ b/README.md
@@ -308,6 +308,10 @@ python scripts/live_inference.py
 ```
 https://github.com/user-attachments/assets/6bc1dc6a-a223-4220-954d-2dab5c75b4a8
 
+The following is an inference using the pre-trained model `deim_hgnetv2_x` trained on COCO. 
+
+https://github.com/user-attachments/assets/77070ea4-8407-4648-ade3-01cacd77b51b
+
 
 For image inference, specify the path to the image file as the input.
 

From 688b4d5499fb9e98bb78ac19e36984edeec8b99b Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 17:20:20 +0800
Subject: [PATCH 30/41] update quickstart

---
 .gitignore                |   1 +
 README.md                 |   2 +-
 detection_result.jpg      | Bin 74649 -> 0 bytes
 nbs/export.ipynb          | 142 +++++++++++++++++++++++++++-----------
 scripts/live_inference.py |  18 +++--
 scripts/quickstart.py     |  27 ++++++++
 src/deimkit/predictor.py  |   2 +-
 7 files changed, 145 insertions(+), 47 deletions(-)
 delete mode 100644 detection_result.jpg

diff --git a/.gitignore b/.gitignore
index 90cd4453..55f2e665 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@ outputs/
 trt_cache/
 # Dataset
 dataset_collections/
+checkpoints/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/README.md b/README.md
index e8878f1b..492c0994 100644
--- a/README.md
+++ b/README.md
@@ -308,7 +308,7 @@ python scripts/live_inference.py
 ```
 https://github.com/user-attachments/assets/6bc1dc6a-a223-4220-954d-2dab5c75b4a8
 
-The following is an inference using the pre-trained model `deim_hgnetv2_x` trained on COCO. 
+The following is an inference using the pre-trained model `deim_hgnetv2_x` trained on COCO. See how I exported the pre-trained model to onnx in this notebook [here](nbs/export.ipynb).
 
 https://github.com/user-attachments/assets/77070ea4-8407-4648-ade3-01cacd77b51b
 
diff --git a/detection_result.jpg b/detection_result.jpg
deleted file mode 100644
index 74a9f557ec6107ce531c42e03fb7752fdaeb55f4..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/nbs/export.ipynb b/nbs/export.ipynb
index e3c7ef99..e1babd4f 100644
--- a/nbs/export.ipynb
+++ b/nbs/export.ipynb
@@ -9,35 +9,107 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-03-31 10:35:25.954\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mUsing device: cpu\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:25.955\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m126\u001b[0m - \u001b[1mLoading checkpoint from /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best.pth\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.278\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m131\u001b[0m - \u001b[1mUsing EMA weights for model export\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m162\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.461\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1mUsing target shape from config: (1, 3, 640, 640)\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mInitialized PreprocessingModule with target size: (640, 640)\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.462\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m201\u001b[0m - \u001b[1mIncluding preprocessing steps in the ONNX model.\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.474\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m260\u001b[0m - \u001b[1mUsing input names: ['input_bgr', 'orig_target_sizes']\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m261\u001b[0m - \u001b[1mUsing output names: ['labels', 'boxes', 'scores']\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.475\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m262\u001b[0m - \u001b[1mUsing dynamic axes: {'input_bgr': {0: 'N', 2: 'H', 3: 'W'}, 'orig_target_sizes': {0: 'N'}, 'labels': {0: 'N'}, 'boxes': {0: 'N'}, 'scores': {0: 'N'}}\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.476\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m263\u001b[0m - \u001b[1mExporting model to ONNX: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.486\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Resized shape: torch.Size([1, 3, 640, 640])\u001b[0m\n",
-      "/home/dnth/Desktop/DEIMKit/src/deimkit/exporter.py:58: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  if x.shape[1] != 3:\n",
-      "\u001b[32m2025-03-31 10:35:26.491\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Swapped BGR to RGB\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:26.493\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m67\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Normalized pixel values to [0, 1]\u001b[0m\n"
+      "\u001b[32m2025-04-02 16:30:26.794\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m119\u001b[0m - \u001b[1mInitializing Predictor with device=auto\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:30:26.835\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_device\u001b[0m:\u001b[36m168\u001b[0m - \u001b[1mAuto-selected device: cuda\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:30:26.878\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_download_checkpoint\u001b[0m:\u001b[36m197\u001b[0m - \u001b[1mDownloading checkpoint for model deim_hgnetv2_x...\u001b[0m\n",
+      "Downloading...\n",
+      "From (original): https://drive.google.com/uc?id=1dPtbgtGgq1Oa7k_LgH1GXPelg1IVeu0j\n",
+      "From (redirected): https://drive.google.com/uc?id=1dPtbgtGgq1Oa7k_LgH1GXPelg1IVeu0j&confirm=t&uuid=b8b8be39-c2e9-4de7-bd7e-4bd5d14ba721\n",
+      "To: /home/dnth/Desktop/DEIMKit/nbs/checkpoints/deim_hgnetv2_x.pth\n",
+      "100%|██████████| 252M/252M [00:56<00:00, 4.50MB/s] \n",
+      "\u001b[32m2025-04-02 16:31:27.994\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_download_checkpoint\u001b[0m:\u001b[36m200\u001b[0m - \u001b[32m\u001b[1mDownloaded checkpoint to checkpoints/deim_hgnetv2_x.pth\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:28.280\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m232\u001b[0m - \u001b[1mLoading configuration from model name: deim_hgnetv2_x\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:28.295\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_setup_model_config\u001b[0m:\u001b[36m240\u001b[0m - \u001b[1mUpdating model configuration for 80 classes\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:28.663\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m_load_model_weights\u001b[0m:\u001b[36m253\u001b[0m - \u001b[1mSuccessfully loaded checkpoint weights\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:28.976\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.predictor\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m155\u001b[0m - \u001b[32m\u001b[1mPredictor initialization complete\u001b[0m\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from deimkit import list_models, load_model\n",
+    "\n",
+    "coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\n",
+    "    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',\n",
+    "    'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',\n",
+    "    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',\n",
+    "    'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\n",
+    "    'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\n",
+    "    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\n",
+    "]\n",
+    "\n",
+    "model = load_model(\"deim_hgnetv2_x\", class_names=coco_classes)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded stage1 B0 HGNetV2 from local file.\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "task: detection\n",
+       "num_workers: 0\n",
+       "batch_size: None\n",
+       "resume: None\n",
+       "tuning: None\n",
+       "epoches: 58\n",
+       "last_epoch: -1\n",
+       "lrsheduler: flatcosine\n",
+       "lr_gamma: 0.5\n",
+       "no_aug_epoch: 8\n",
+       "warmup_iter: 2000\n",
+       "flat_epoch: 29\n",
+       "use_amp: True\n",
+       "use_ema: True\n",
+       "ema_decay: 0.9999\n",
+       "ema_warmups: 2000\n",
+       "sync_bn: True\n",
+       "clip_max_norm: 0.1\n",
+       "find_unused_parameters: False\n",
+       "seed: None\n",
+       "print_freq: 100\n",
+       "checkpoint_freq: 4\n",
+       "output_dir: ./outputs/deim_hgnetv2_x_coco\n",
+       "summary_dir: None\n",
+       "device: \n",
+       "yaml_cfg: {'task': 'detection', 'evaluator': {'type': 'CocoEvaluator', 'iou_types': ['bbox']}, 'num_classes': 80, 'remap_mscoco_category': True, 'train_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': '/datassd/COCO/train2017/', 'ann_file': '/datassd/COCO/annotations/instances_train2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Mosaic', 'output_size': 640, 'rotation_range': 10, 'translation_range': [0.1, 0.1], 'scaling_range': [0.5, 1.5], 'probability': 1.0, 'fill_value': 0, 'use_cache': False, 'max_cached_images': 50, 'random_pop': True}, {'type': 'RandomPhotometricDistort', 'p': 0.5}, {'type': 'RandomZoomOut', 'fill': 0}, {'type': 'RandomIoUCrop', 'p': 0.8}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'RandomHorizontalFlip'}, {'type': 'Resize', 'size': (640, 640)}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}, {'type': 'ConvertBoxes', 'fmt': 'cxcywh', 'normalize': True}], 'policy': {'name': 'stop_epoch', 'epoch': [4, 29, 50], 'ops': ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']}, 'mosaic_prob': 0.5}}, 'shuffle': True, 'num_workers': 4, 'drop_last': True, 'collate_fn': {'type': 'BatchImageCollateFunction', 'base_size': 640, 'base_size_repeat': 3, 'stop_epoch': 50, 'ema_restart_decay': 0.9998, 'mixup_prob': 0.5, 'mixup_epochs': [4, 29]}, 'total_batch_size': 32}, 'val_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': '/datassd/COCO/val2017/', 'ann_file': '/datassd/COCO/annotations/instances_val2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Resize', 'size': (640, 640)}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}]}}, 'shuffle': False, 'num_workers': 4, 'drop_last': False, 'collate_fn': {'type': 'BatchImageCollateFunction'}, 'total_batch_size': 64}, 'print_freq': 100, 'output_dir': './outputs/deim_hgnetv2_x_coco', 'checkpoint_freq': 4, 'sync_bn': True, 'find_unused_parameters': False, 'use_amp': True, 'scaler': {'type': 'GradScaler', 'enabled': True}, 'use_ema': True, 'ema': {'type': 'ModelEMA', 'decay': 0.9999, 'warmups': 1000, 'start': 0}, 'epoches': 58, 'clip_max_norm': 0.1, 'optimizer': {'type': 'AdamW', 'params': [{'params': '^(?=.*backbone)(?!.*norm|bn).*$', 'lr': 5e-06}, {'params': '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$', 'weight_decay': 0.0}], 'lr': 0.0005, 'betas': [0.9, 0.999], 'weight_decay': 0.000125}, 'lr_scheduler': {'type': 'MultiStepLR', 'milestones': [500], 'gamma': 0.1}, 'lr_warmup_scheduler': {'type': 'LinearWarmup', 'warmup_duration': 500}, 'model': 'DEIM', 'criterion': 'DEIMCriterion', 'postprocessor': 'PostProcessor', 'use_focal_loss': True, 'eval_spatial_size': (640, 640), 'DEIM': {'backbone': 'HGNetv2', 'encoder': 'HybridEncoder', 'decoder': 'DFINETransformer'}, 'lrsheduler': 'flatcosine', 'lr_gamma': 0.5, 'warmup_iter': 2000, 'flat_epoch': 29, 'no_aug_epoch': 8, 'HGNetv2': {'pretrained': False, 'local_model_dir': '../RT-DETR-main/D-FINE/weight/hgnetv2/', 'name': 'B5', 'return_idx': [1, 2, 3], 'freeze_stem_only': True, 'freeze_at': -1, 'freeze_norm': False}, 'HybridEncoder': {'in_channels': [512, 1024, 2048], 'feat_strides': [8, 16, 32], 'hidden_dim': 384, 'use_encoder_idx': [2], 'num_encoder_layers': 1, 'nhead': 8, 'dim_feedforward': 2048, 'dropout': 0.0, 'enc_act': 'gelu', 'expansion': 1.0, 'depth_mult': 1, 'act': 'silu'}, 'DFINETransformer': {'feat_channels': [384, 384, 384], 'feat_strides': [8, 16, 32], 'hidden_dim': 256, 'num_levels': 3, 'num_layers': 6, 'eval_idx': -1, 'num_queries': 300, 'num_denoising': 100, 'label_noise_ratio': 0.5, 'box_noise_scale': 1.0, 'reg_max': 32, 'reg_scale': 8, 'layer_scale': 1, 'num_points': [3, 6, 3], 'cross_attn_method': 'default', 'query_select_method': 'default', 'activation': 'silu', 'mlp_act': 'silu'}, 'PostProcessor': {'num_top_queries': 300}, 'DEIMCriterion': {'weight_dict': {'loss_vfl': 1, 'loss_bbox': 5, 'loss_giou': 2, 'loss_fgl': 0.15, 'loss_ddf': 1.5, 'loss_mal': 1}, 'losses': ['mal', 'boxes', 'local'], 'alpha': 0.75, 'gamma': 1.5, 'reg_max': 32, 'matcher': {'type': 'HungarianMatcher', 'weight_dict': {'cost_class': 2, 'cost_bbox': 5, 'cost_giou': 2}, 'alpha': 0.25, 'gamma': 2.0}}, '__include__': ['./dfine_hgnetv2_x_coco.yml', '../base/deim.yml']}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.cfg.save(\"./checkpoints/deim_hgnetv2_x.yml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "\u001b[32m2025-04-02 16:31:29.014\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m123\u001b[0m - \u001b[1mUsing device: cpu\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.014\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m126\u001b[0m - \u001b[1mLoading checkpoint from ./checkpoints/deim_hgnetv2_x.pth\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.174\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m134\u001b[0m - \u001b[1mEMA weights not found, using regular model weights\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m162\u001b[0m - \u001b[1mInput shape not provided, getting size from config\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.620\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m188\u001b[0m - \u001b[1mUsing target shape from config: (1, 3, 640, 640)\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.621\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m32\u001b[0m - \u001b[1mInitialized PreprocessingModule with target size: (640, 640)\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.621\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m201\u001b[0m - \u001b[1mIncluding preprocessing steps in the ONNX model.\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.640\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m260\u001b[0m - \u001b[1mUsing input names: ['input_bgr', 'orig_target_sizes']\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m261\u001b[0m - \u001b[1mUsing output names: ['labels', 'boxes', 'scores']\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m262\u001b[0m - \u001b[1mUsing dynamic axes: {'input_bgr': {0: 'N', 2: 'H', 3: 'W'}, 'orig_target_sizes': {0: 'N'}, 'labels': {0: 'N'}, 'boxes': {0: 'N'}, 'scores': {0: 'N'}}\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.641\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m263\u001b[0m - \u001b[1mExporting model to ONNX: ./checkpoints/deim_hgnetv2_x.onnx\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.662\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m54\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Resized shape: torch.Size([1, 3, 640, 640])\u001b[0m\n",
+      "/home/dnth/Desktop/DEIMKit/src/deimkit/exporter.py:58: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  if x.shape[1] != 3:\n",
+      "\u001b[32m2025-04-02 16:31:29.666\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m62\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Swapped BGR to RGB\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:29.667\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mforward\u001b[0m:\u001b[36m67\u001b[0m - \u001b[34m\u001b[1mPreprocessing: Normalized pixel values to [0, 1]\u001b[0m\n",
       "/home/dnth/Desktop/DEIMKit/src/deimkit/engine/deim/dfine_decoder.py:646: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
       "  if memory.shape[0] > 1:\n",
       "/home/dnth/Desktop/DEIMKit/src/deimkit/engine/deim/dfine_decoder.py:129: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
@@ -50,11 +122,11 @@
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
       "/home/dnth/Desktop/DEIMKit/.pixi/envs/cuda/lib/python3.11/site-packages/torch/onnx/utils.py:1127: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at /pytorch/torch/csrc/jit/passes/onnx/constant_fold.cpp:178.)\n",
       "  _C._jit_pass_onnx_graph_shape_type_inference(\n",
-      "\u001b[32m2025-03-31 10:35:28.011\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m285\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:28.011\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m296\u001b[0m - \u001b[1mSimplifying ONNX model with input shapes: {'input_bgr': torch.Size([1, 3, 640, 640]), 'orig_target_sizes': torch.Size([1, 2])}\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:28.050\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m401\u001b[0m - \u001b[1mSimplifying ONNX model: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx -> /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:30.128\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m411\u001b[0m - \u001b[32m\u001b[1mONNX model simplification successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n",
-      "\u001b[32m2025-03-31 10:35:30.207\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m368\u001b[0m - \u001b[1mONNX model validation successful: /home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\u001b[0m\n"
+      "\u001b[32m2025-04-02 16:31:34.042\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m285\u001b[0m - \u001b[32m\u001b[1mONNX export completed successfully: ./checkpoints/deim_hgnetv2_x.onnx\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:34.043\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36mto_onnx\u001b[0m:\u001b[36m296\u001b[0m - \u001b[1mSimplifying ONNX model with input shapes: {'input_bgr': torch.Size([1, 3, 640, 640]), 'orig_target_sizes': torch.Size([1, 2])}\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:34.077\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m401\u001b[0m - \u001b[1mSimplifying ONNX model: ./checkpoints/deim_hgnetv2_x.onnx -> ./checkpoints/deim_hgnetv2_x.onnx\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:49.184\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_simplify_onnx_model\u001b[0m:\u001b[36m411\u001b[0m - \u001b[32m\u001b[1mONNX model simplification successful: ./checkpoints/deim_hgnetv2_x.onnx\u001b[0m\n",
+      "\u001b[32m2025-04-02 16:31:49.575\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mdeimkit.exporter\u001b[0m:\u001b[36m_check_onnx_model\u001b[0m:\u001b[36m368\u001b[0m - \u001b[1mONNX model validation successful: ./checkpoints/deim_hgnetv2_x.onnx\u001b[0m\n"
      ]
     }
    ],
@@ -62,23 +134,15 @@
     "from deimkit.exporter import Exporter\n",
     "from deimkit.config import Config\n",
     "\n",
-    "config = Config(\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100/config.yml\")\n",
+    "config = Config(\"./checkpoints/deim_hgnetv2_x.yml\")\n",
     "exporter = Exporter(config)\n",
     "\n",
     "output_path = exporter.to_onnx(\n",
-    "    checkpoint_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best.pth\",\n",
-    "    output_path=\"/home/dnth/Desktop/DEIMKit/outputs/rock-paper-scissors/deim_hgnetv2_s_30ep_640px_num_queries_100_lr_gamma_0.1/best_prep.onnx\",\n",
-    "    include_preprocessing=True\n",
+    "    checkpoint_path=\"./checkpoints/deim_hgnetv2_x.pth\",\n",
+    "    output_path=\"./checkpoints/deim_hgnetv2_x.onnx\"\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/scripts/live_inference.py b/scripts/live_inference.py
index 1d2835dd..3070b4f6 100644
--- a/scripts/live_inference.py
+++ b/scripts/live_inference.py
@@ -512,10 +512,15 @@ def run_inference_video(
     frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
-    # Create video writer
+    # Calculate output dimensions based on video_width
+    scale = video_width / frame_width
+    output_width = video_width
+    output_height = int(frame_height * scale)
+
+    # Create video writer with new dimensions
     output_path = "detection_output.mp4"
     fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+    out = cv2.VideoWriter(output_path, fourcc, fps, (output_width, output_height))
 
     # Initialize FPS calculation
     prev_time = time.time()
@@ -595,8 +600,12 @@ def run_inference_video(
                 class_names=class_names,
             )
 
+            # Before writing the frame, resize it
+            result_image = cv2.resize(result_image, (output_width, output_height))
+            out.write(result_image)
+
             # Add video width display at top left with dark green background
-            width_text = f"Width: {frame_width}px"
+            width_text = f"Width: {output_width}px"
             text_size = cv2.getTextSize(width_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
             
             # Draw dark green background rectangle
@@ -673,9 +682,6 @@ def run_inference_video(
                 2,
             )
 
-            # Write frame to output video
-            out.write(result_image)
-
             # Display frame (optional)
             cv2.imshow("Video Detection", result_image)
             if cv2.waitKey(1) & 0xFF == ord("q"):
diff --git a/scripts/quickstart.py b/scripts/quickstart.py
index ce6984eb..b1ad5eb7 100644
--- a/scripts/quickstart.py
+++ b/scripts/quickstart.py
@@ -71,3 +71,30 @@ def download_and_unzip(url, extract_to="./"):
 
 logger.info("Testing predictions...")
 predictions = model.predict("./data/coco8-converted/000000000009.jpg")
+
+logger.info("Exporting pretrained model to ONNX...")
+
+coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
+    'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
+    'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
+    'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+model = load_model("deim_hgnetv2_n", class_names=coco_classes)
+model.cfg.save("./checkpoints/deim_hgnetv2_n.yml")
+
+from deimkit.exporter import Exporter
+from deimkit.config import Config
+
+config = Config("./checkpoints/deim_hgnetv2_n.yml")
+exporter = Exporter(config)
+
+output_path = exporter.to_onnx(
+    checkpoint_path="./checkpoints/deim_hgnetv2_n.pth",
+    output_path="./checkpoints/deim_hgnetv2_n.onnx"
+)
+
+logger.info(f"ONNX model saved to {output_path}")
diff --git a/src/deimkit/predictor.py b/src/deimkit/predictor.py
index 6026c55c..95b5549a 100644
--- a/src/deimkit/predictor.py
+++ b/src/deimkit/predictor.py
@@ -23,7 +23,7 @@
     "deim_hgnetv2_x": "1dPtbgtGgq1Oa7k_LgH1GXPelg1IVeu0j",  # XLarge model
 }
 
-DEFAULT_CACHE_DIR = os.path.expanduser("~/.cache/deim/checkpoints")
+DEFAULT_CACHE_DIR = os.path.expanduser("./checkpoints")
 DEFAULT_IMAGE_SIZE = (640, 640)
 DEFAULT_MEAN = [0.485, 0.456, 0.406]
 DEFAULT_STD = [0.229, 0.224, 0.225]

From 158b7934cbc953bc1c116abec41f4af6ee9ffaeb Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 17:23:55 +0800
Subject: [PATCH 31/41] add credit

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 492c0994..903e1e2a 100644
--- a/README.md
+++ b/README.md
@@ -236,7 +236,7 @@ output_path = exporter.to_onnx(
 ```
 
 > [!NOTE]
-> The exported model will accept raw BGR images of any size. It will also handle the preprocessing internally.
+> The exported model will accept raw BGR images of any size. It will also handle the preprocessing internally. Credit to [PINTO0309](https://github.com/PINTO0309/DEIM) for the implementation.
 > 
 > ![onnx model](assets/exported_onnx.png)
 

From fc1ace471acb315cf437262efe28a7e6a9a0e02e Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 18:44:36 +0800
Subject: [PATCH 32/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 903e1e2a..3e1ee08c 100644
--- a/README.md
+++ b/README.md
@@ -272,7 +272,7 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
-Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images.
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
 
 https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
 

From 55786404d64fa7048787b34614847b515d3a0555 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 18:46:50 +0800
Subject: [PATCH 33/41] update

---
 README.md | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 3e1ee08c..c3b2a4b4 100644
--- a/README.md
+++ b/README.md
@@ -274,21 +274,23 @@ python scripts/live_inference.py
 
 Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
 
-https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
+Webcam video width at 1920x1080 pixels:
 
+https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
 
+Webcam video width at 1280x720 pixels:
 
 https://github.com/user-attachments/assets/31a8644e-e0c6-4bba-9d4f-857a3d0b53e1
 
-
+Webcam video width at 848x480 pixels:
 
 https://github.com/user-attachments/assets/aa267f05-5dbd-4824-973c-62f3b8f59c80
 
-
+Webcam video width at 640x480 pixels:
 
 https://github.com/user-attachments/assets/3d0c04c0-645a-4d54-86c0-991930491113
 
-
+Webcam video width at 320x240 pixels:
 
 https://github.com/user-attachments/assets/f4afff9c-3e6d-4965-ab86-0d4de7ce1a44
 

From 083c802d63d4023092731a6b243b2e1337421651 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 18:47:31 +0800
Subject: [PATCH 34/41] update

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index c3b2a4b4..3943a506 100644
--- a/README.md
+++ b/README.md
@@ -274,23 +274,23 @@ python scripts/live_inference.py
 
 Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
 
-Webcam video width at 1920x1080 pixels:
+Webcam video width at 1920x1080 pixels (1080p):
 
 https://github.com/user-attachments/assets/bd98eb1e-feff-4b53-9fa9-d4aff6a724e0
 
-Webcam video width at 1280x720 pixels:
+Webcam video width at 1280x720 pixels (720p):
 
 https://github.com/user-attachments/assets/31a8644e-e0c6-4bba-9d4f-857a3d0b53e1
 
-Webcam video width at 848x480 pixels:
+Webcam video width at 848x480 pixels (480p):
 
 https://github.com/user-attachments/assets/aa267f05-5dbd-4824-973c-62f3b8f59c80
 
-Webcam video width at 640x480 pixels:
+Webcam video width at 640x480 pixels (480p):
 
 https://github.com/user-attachments/assets/3d0c04c0-645a-4d54-86c0-991930491113
 
-Webcam video width at 320x240 pixels:
+Webcam video width at 320x240 pixels (240p):
 
 https://github.com/user-attachments/assets/f4afff9c-3e6d-4965-ab86-0d4de7ce1a44
 

From eaf1bf4851a7a06185be971485b10b2844906e7c Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 18:48:26 +0800
Subject: [PATCH 35/41] update

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3943a506..40021fa8 100644
--- a/README.md
+++ b/README.md
@@ -272,7 +272,9 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
-Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. Here are some examples of inference on a webcam at different video widths. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
+
+Here are some examples of inference on a webcam at different video widths.
 
 Webcam video width at 1920x1080 pixels (1080p):
 

From 66b6ea972b379f96dc34ce90deb25dd96ab44c75 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 18:49:06 +0800
Subject: [PATCH 36/41] update

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 40021fa8..ca4fd7fe 100644
--- a/README.md
+++ b/README.md
@@ -272,7 +272,7 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
-Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS as it uses more efficient onnx operators. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
 
 Here are some examples of inference on a webcam at different video widths.
 

From 3f09ecc413ad1d126d7171e958271b43bc9704d9 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 19:33:07 +0800
Subject: [PATCH 37/41] update readme

---
 README.md | 67 ++++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 52 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index ca4fd7fe..7539e2a8 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,44 @@
 <p>DEIMKit is a Python wrapper for <a href="https://github.com/ShihuaHuang95/DEIM">DEIM: DETR with Improved Matching for Fast Convergence</a>. Check out the original repo for more details.</p>
 </div>
 
+
+
+<!-- Add HTML Table of Contents -->
+<div align="center">
+    <br />
+    <table>
+        <tr>
+            <td align="center">
+                <a href="#why-deimkit">🤔 Why DEIMKit?</a>
+            </td>
+            <td align="center">
+                <a href="#supported-features">🌟 Features</a>
+            </td>
+            <td align="center">
+                <a href="#installation">📦 Installation</a>
+            </td>
+            <td align="center">
+                <a href="#usage">🚀 Usage</a>
+            </td>
+        </tr>
+        <tr>
+            <td align="center">
+                <a href="#inference">💡 Inference</a>
+            </td>
+            <td align="center">
+                <a href="#training">🏋️ Training</a>
+            </td>
+            <td align="center">
+                <a href="#export">💾 Export</a>
+            </td>
+             <td align="center">
+                <a href="#disclaimer">⚠️ Disclaimer</a>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<br />
 <div align="center">
   <a href="https://colab.research.google.com/github/dnth/DEIMKit/blob/main/nbs/colab-quickstart.ipynb">
     <img src="https://img.shields.io/badge/Open%20In-Colab-blue?style=for-the-badge&logo=google-colab" alt="Open In Colab"/>
@@ -22,23 +60,22 @@
 </div>
 </div>
 
-
-## Why DEIMKit?
+## 🤔 Why DEIMKit?
 
 - **Pure Python Configuration** - No complicated YAML files, just clean Python code
 - **Cross-Platform Simplicity** - Single command installation on Linux, macOS, and Windows
 - **Intuitive API** - Load, train, predict, export in just a few lines of code
 
-## Supported Features
+## 🌟 Supported Features
 
 - [x] Inference
 - [x] Training
 - [x] Export
 
 
-## Installation
+## 📦 Installation
 
-### Using pip
+### 📥 Using pip
 If you're installing using pip, install [torch](https://pytorch.org/get-started/locally/) and torchvision as a pre-requisite.
 
 Next, install the package.
@@ -52,7 +89,7 @@ Stable version
 pip install git+https://github.com/dnth/DEIM.git@v0.1.1
 ```
 
-### Using Pixi
+### 🔌 Using Pixi
 
 > [!TIP] 
 > I recommend using [Pixi](https://pixi.sh) to run this package. Pixi makes it easy to install the right version of Python and the dependencies to run this package on any platform!
@@ -80,7 +117,7 @@ This will download a toy dataset with 8 images, and train a model on it for 3 ep
 
 If this runs without any issues, you've got a working Python environment with all the dependencies installed. This also installs DEIMKit in editable mode for development. See the [pixi cheatsheet](#-pixi-cheat-sheet) below for more. 
 
-## Usage
+## 🚀 Usage
 
 List models supported by DEIMKit
 
@@ -98,7 +135,7 @@ list_models()
  'deim_hgnetv2_x']
 ```
 
-### Inference
+### 💡 Inference
 
 Load a pretrained model by the original authors
 
@@ -152,7 +189,7 @@ Stomata Dataset
 
 See the [demo notebook on using pretrained models](nbs/pretrained-model-inference.ipynb) and [custom model inference](nbs/custom-model-inference.ipynb) for more details.
 
-### Training
+### 🏋️ Training
 
 DEIMKit provides a simple interface for training your own models.
 
@@ -220,7 +257,7 @@ Navigate to the http://localhost:6006/ in your browser to view the training prog
 
 ![alt text](assets/tensorboard.png)
 
-### Export
+### 💾 Export
 
 ```python
 from deimkit.exporter import Exporter
@@ -240,7 +277,7 @@ output_path = exporter.to_onnx(
 > 
 > ![onnx model](assets/exported_onnx.png)
 
-### Gradio App
+### 🖥️ Gradio App
 Run a Gradio app to interact with your model. The app will accept raw BGR images of any size. It will also handle the preprocessing internally using the exported ONNX model.
 
 ```bash
@@ -256,9 +293,9 @@ python scripts/gradio_demo.py \
 > This means you can use any image size you want. Play around with the input size slider to see what works best for your model. 
 > Some objects are visible even at lower input sizes, this means you can use a lower input size to speed up inference.
 
-### Live Inference
+### 🎥 Live Inference
 Run live inference on a video, image or webcam using ONNXRuntime. This runs on CPU by default.
-If you would like to use the CUDA backend, you can install the `onnxruntime-gpu` package and uninstall the `onnxruntime` package.
+If you would like to use the CUDA backend, install the `onnxruntime-gpu` package and uninstall the `onnxruntime` package.
 
 For running inference on a webcam, set the `--webcam` flag.
 
@@ -350,7 +387,7 @@ The following is a demo of image inference
 > If you want to use the CPU, replace `cuda` with `cpu` in the command above.
 
 
-## Pixi Cheat Sheet
+## 📝 Pixi Cheat Sheet
 Here are some useful tasks you can run with Pixi.
 
 Run a quickstart
@@ -408,5 +445,5 @@ pixi run export --config config.yml --checkpoint model.pth --output model.onnx
 
 
 
-## Disclaimer
+## ⚠️ Disclaimer
 I'm not affiliated with the original DEIM authors. I just found the model interesting and wanted to try it out. The changes made here are of my own. Please cite and star the original repo if you find this useful.

From cd17ba87a7b99f760d024974781bb4ec1ff5172f Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 19:34:01 +0800
Subject: [PATCH 38/41] add dash

---
 README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 7539e2a8..e1233958 100644
--- a/README.md
+++ b/README.md
@@ -20,30 +20,30 @@
     <table>
         <tr>
             <td align="center">
-                <a href="#why-deimkit">🤔 Why DEIMKit?</a>
+                <a href="#-why-deimkit">🤔 Why DEIMKit?</a>
             </td>
             <td align="center">
-                <a href="#supported-features">🌟 Features</a>
+                <a href="#-supported-features">🌟 Features</a>
             </td>
             <td align="center">
-                <a href="#installation">📦 Installation</a>
+                <a href="#-installation">📦 Installation</a>
             </td>
             <td align="center">
-                <a href="#usage">🚀 Usage</a>
+                <a href="#-usage">🚀 Usage</a>
             </td>
         </tr>
         <tr>
             <td align="center">
-                <a href="#inference">💡 Inference</a>
+                <a href="#-inference">💡 Inference</a>
             </td>
             <td align="center">
-                <a href="#training">🏋️ Training</a>
+                <a href="#-training">🏋️ Training</a>
             </td>
             <td align="center">
-                <a href="#export">💾 Export</a>
+                <a href="#-export">💾 Export</a>
             </td>
              <td align="center">
-                <a href="#disclaimer">⚠️ Disclaimer</a>
+                <a href="#-disclaimer">⚠️ Disclaimer</a>
             </td>
         </tr>
     </table>

From 09f87edef3d283c6311e9821a8ab722dec554823 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 19:37:07 +0800
Subject: [PATCH 39/41] update key features

---
 README.md | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index e1233958..23580905 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@
                 <a href="#-why-deimkit">🤔 Why DEIMKit?</a>
             </td>
             <td align="center">
-                <a href="#-supported-features">🌟 Features</a>
+                <a href="#-key-features">🌟 Key Features</a>
             </td>
             <td align="center">
                 <a href="#-installation">📦 Installation</a>
@@ -66,12 +66,24 @@
 - **Cross-Platform Simplicity** - Single command installation on Linux, macOS, and Windows
 - **Intuitive API** - Load, train, predict, export in just a few lines of code
 
-## 🌟 Supported Features
-
-- [x] Inference
-- [x] Training
-- [x] Export
-
+## 🌟 Key Features
+
+*   **💡 Inference**
+    *   [x] Single Image & Batch Prediction
+    *   [x] Load Pretrained & Custom Models
+    *   [x] Built-in Result Visualization
+    *   [x] Live ONNX Inference (Webcam, Video, Image)
+*   **🏋️ Training**
+    *   [x] Single & Multi-GPU Training
+    *   [x] Custom Dataset Support (COCO Format)
+    *   [x] Flexible Configuration via Pure Python
+*   **💾 Export**
+    *   [x] Export Trained Models to ONNX
+    *   [x] ONNX Model with Integrated Preprocessing
+*   **🛠️ Utilities & Demos**
+    *   [x] Cross-Platform Support (Linux, macOS, Windows)
+    *   [x] Pixi Environment Management Integration
+    *   [x] Interactive Gradio Demo Script
 
 ## 📦 Installation
 

From 7180e6dd51af9150e59223dc3b7ff996ad263d73 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 19:38:54 +0800
Subject: [PATCH 40/41] updte

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 23580905..7b91d589 100644
--- a/README.md
+++ b/README.md
@@ -321,9 +321,9 @@ python scripts/live_inference.py
     --threshold 0.3             # Detection confidence threshold
 ```
 
-Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS as it uses more efficient onnx operators. The following is a model I trained on a custom dataset using the deim_hgnetv2_s model.
+Because we are handling the preprocessing internally in the ONNX model, the input size is not limited to the original 640x640. You can use any input size you want for inference. The model was trained on 640x640 images. Integrating the preprocessing internally in the ONNX model also lets us run inference at very high FPS as it uses more efficient onnx operators. 
 
-Here are some examples of inference on a webcam at different video widths.
+The following is a model I trained on a custom dataset using the deim_hgnetv2_s model and exported to ONNX. Here are some examples of inference on a webcam at different video resolutions.
 
 Webcam video width at 1920x1080 pixels (1080p):
 

From a47979a8b6d0f24cfd44b3ef7ebecb256b4f75c2 Mon Sep 17 00:00:00 2001
From: dnth <dickson.neoh@gmail.com>
Date: Wed, 2 Apr 2025 19:51:42 +0800
Subject: [PATCH 41/41] add openvino export

---
 README.md | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/README.md b/README.md
index 7b91d589..aadba3e2 100644
--- a/README.md
+++ b/README.md
@@ -270,6 +270,7 @@ Navigate to the http://localhost:6006/ in your browser to view the training prog
 ![alt text](assets/tensorboard.png)
 
 ### 💾 Export
+Currently, the export function is only used for exporting the model to ONNX and run it using ONNXRuntime (see [Live Inference](#-live-inference) for more details). I think one could get pretty far with this even on a low resource machine. Drop an issue if you think this should be extended to other formats.
 
 ```python
 from deimkit.exporter import Exporter
@@ -289,6 +290,29 @@ output_path = exporter.to_onnx(
 > 
 > ![onnx model](assets/exported_onnx.png)
 
+> [!TIP]
+> If you want to export to OpenVINO you can do so directly from the ONNX model.
+> 
+>
+> ```python
+> import onnx
+> from onnx import helper
+> 
+> model = onnx.load("best.onnx")
+>
+> # Change the mode attribute of the GridSample node to bilinear as this operation is not supported in OpenVINO
+> for node in model.graph.node:
+>     if node.op_type == 'GridSample':
+>         for i, attr in enumerate(node.attribute):
+>             if attr.name == 'mode' and attr.s == b'linear':
+>                 # Replace 'linear' with 'bilinear'
+>                 node.attribute[i].s = b'bilinear'
+>       
+> # Save the modified model
+> onnx.save(model, "best_prep_openvino.onnx")
+> ```
+> You can then use the live inference script to run inference on the OpenVINO model.
+
 ### 🖥️ Gradio App
 Run a Gradio app to interact with your model. The app will accept raw BGR images of any size. It will also handle the preprocessing internally using the exported ONNX model.