diff --git a/focoos/data/catalog/catalog.py b/focoos/data/catalog/catalog.py
index 52891af3..1b8b6fe7 100644
--- a/focoos/data/catalog/catalog.py
+++ b/focoos/data/catalog/catalog.py
@@ -85,6 +85,19 @@ class CatalogDataset:
             filter_empty=False,
         ),
     ),
+    CatalogDataset(
+        name="coco_2017_cls",
+        task=Task.CLASSIFICATION,
+        train_split=CatalogSplit(
+            image_root="coco/train2017",
+            json_file="coco/annotations/instances_train2017.json",
+        ),
+        val_split=CatalogSplit(
+            image_root="coco/val2017",
+            json_file="coco/annotations/instances_val2017.json",
+            filter_empty=False,
+        ),
+    ),
     CatalogDataset(
         name="coco_2017_instance",
         task=Task.INSTANCE_SEGMENTATION,
@@ -131,6 +144,7 @@ def _load_dataset_split(
     split_name: str,
     split: CatalogSplit,
     task: Task,
+    split_type: DatasetSplitType,
     root=DATASETS_DIR,
 ) -> DictDataset:
     """
@@ -152,7 +166,7 @@ def get_path(root, path):
         task=task,
     )
 
-    if task in [Task.DETECTION, Task.INSTANCE_SEGMENTATION, Task.KEYPOINT]:
+    if task in [Task.DETECTION, Task.INSTANCE_SEGMENTATION, Task.KEYPOINT, Task.CLASSIFICATION]:
         dataset_dict = load_coco_json(json_file_path, image_root_path, metadata, task=task)
         if split.filter_empty:
             dataset_dict = filter_images_with_only_crowd_annotations(dataset_dicts=dataset_dict)
@@ -171,7 +185,7 @@ def get_path(root, path):
         raise ValueError(f"Unknown task {task}")
 
     metadata.count = len(dataset_dict)
-    return DictDataset(dataset_dict, task=task, metadata=metadata)
+    return DictDataset(dataset_dict, task=task, metadata=metadata, split_type=split_type)
 
 
 def get_dataset_split(name: str, split: DatasetSplitType, datasets_root=DATASETS_DIR) -> DictDataset:
@@ -192,4 +206,4 @@ def get_dataset_split(name: str, split: DatasetSplitType, datasets_root=DATASETS
     else:
         raise ValueError(f"Unknown split {split}")
 
-    return _load_dataset_split(split_name, entry, ds.task, datasets_root)
+    return _load_dataset_split(split_name=split_name, split=entry, task=ds.task, split_type=split, root=datasets_root)
diff --git a/focoos/infer/runtimes/torchscript.py b/focoos/infer/runtimes/torchscript.py
index ac0c5c15..619e0da5 100644
--- a/focoos/infer/runtimes/torchscript.py
+++ b/focoos/infer/runtimes/torchscript.py
@@ -49,7 +49,9 @@ def __init__(
 
         if self.opts.warmup_iter > 0:
             size = (
-                self.model_info.im_size if self.model_info.task == Task.DETECTION and self.model_info.im_size else 640
+                self.model_info.im_size
+                if self.model_info.task in [Task.DETECTION, Task.CLASSIFICATION] and self.model_info.im_size
+                else 640
             )
             logger.info(f"⏱️ Warming up model {self.model_info.name} on {self.device}, size: {size}x{size}..")
             with torch.no_grad():
diff --git a/focoos/model_registry/fai-cls-m-coco.json b/focoos/model_registry/fai-cls-m-coco.json
index 40b6f3a8..87ff813a 100644
--- a/focoos/model_registry/fai-cls-m-coco.json
+++ b/focoos/model_registry/fai-cls-m-coco.json
@@ -89,23 +89,16 @@
         "num_classes": 80,
         "backbone_config": {
             "use_pretrained": false,
+            "backbone_url": "https://public.focoos.ai/pretrained_models/backbones/stdc_small.pth",
             "model_type": "stdc",
             "in_chans": 3,
-            "base": 64,
-            "layers": [
-                4,
-                5,
-                3
-            ],
             "out_features": [
                 "res2",
                 "res3",
                 "res4",
                 "res5"
             ],
-            "block_num": 4,
-            "block_type": "cat",
-            "size": "large",
+            "size": "small",
             "use_conv_last": false
         },
         "resolution": 224,
@@ -138,252 +131,283 @@
     "weights_uri": "https://public.focoos.ai/pretrained_models/fai-cls-m-coco/model_final.pth",
     "val_dataset": "coco_2017_det",
     "val_metrics": {
-        "classification/F1": 66.983,
-        "classification/F1-airplane": 92.3913,
-        "classification/F1-apple": 54.2636,
-        "classification/F1-backpack": 42.4691,
-        "classification/F1-banana": 65.1429,
-        "classification/F1-baseball bat": 76.4706,
-        "classification/F1-baseball glove": 84.3602,
-        "classification/F1-bear": 87.7551,
-        "classification/F1-bed": 76.3636,
-        "classification/F1-bench": 54.6392,
-        "classification/F1-bicycle": 62.3482,
-        "classification/F1-bird": 63.7168,
-        "classification/F1-boat": 77.9343,
-        "classification/F1-book": 54.5918,
-        "classification/F1-bottle": 54.7368,
-        "classification/F1-bowl": 56.8421,
-        "classification/F1-broccoli": 74.6667,
-        "classification/F1-bus": 79.5322,
-        "classification/F1-cake": 65.6085,
-        "classification/F1-car": 68.8822,
-        "classification/F1-carrot": 60.274,
-        "classification/F1-cat": 81.1321,
-        "classification/F1-cell phone": 41.0714,
-        "classification/F1-chair": 64.7737,
-        "classification/F1-clock": 68.3908,
-        "classification/F1-couch": 73.8916,
-        "classification/F1-cow": 62.3188,
-        "classification/F1-cup": 56.2414,
-        "classification/F1-dining table": 67.8243,
-        "classification/F1-dog": 73.8854,
-        "classification/F1-donut": 62.2951,
-        "classification/F1-elephant": 93.8547,
-        "classification/F1-fire hydrant": 70.7483,
-        "classification/F1-fork": 48.4375,
-        "classification/F1-frisbee": 68.5083,
-        "classification/F1-giraffe": 96.5174,
+        "classification/F1": 61.9222,
+        "classification/F1-airplane": 81.7734,
+        "classification/F1-apple": 45.8015,
+        "classification/F1-backpack": 35.1807,
+        "classification/F1-banana": 57.5916,
+        "classification/F1-baseball bat": 71.2042,
+        "classification/F1-baseball glove": 81.4815,
+        "classification/F1-bear": 83.5165,
+        "classification/F1-bed": 63.9405,
+        "classification/F1-bench": 45.4545,
+        "classification/F1-bicycle": 54.918,
+        "classification/F1-bird": 57.5221,
+        "classification/F1-boat": 74.1071,
+        "classification/F1-book": 48.2916,
+        "classification/F1-bottle": 44.6178,
+        "classification/F1-bowl": 50.8287,
+        "classification/F1-broccoli": 77.027,
+        "classification/F1-bus": 69.4362,
+        "classification/F1-cake": 53.2609,
+        "classification/F1-car": 66.3616,
+        "classification/F1-carrot": 45.5172,
+        "classification/F1-cat": 78.481,
+        "classification/F1-cell phone": 44.6927,
+        "classification/F1-chair": 60.4496,
+        "classification/F1-clock": 67.8161,
+        "classification/F1-couch": 65.9898,
+        "classification/F1-cow": 60.4651,
+        "classification/F1-cup": 50.2674,
+        "classification/F1-dining table": 65.7229,
+        "classification/F1-dog": 63.5762,
+        "classification/F1-donut": 52.9412,
+        "classification/F1-elephant": 91.6201,
+        "classification/F1-fire hydrant": 67.1429,
+        "classification/F1-fork": 37.2093,
+        "classification/F1-frisbee": 71.8954,
+        "classification/F1-giraffe": 93.9394,
         "classification/F1-hair drier": 0.0,
-        "classification/F1-handbag": 47.8326,
-        "classification/F1-horse": 81.4815,
-        "classification/F1-hot dog": 34.9206,
-        "classification/F1-keyboard": 73.4043,
-        "classification/F1-kite": 88.5057,
-        "classification/F1-knife": 42.5532,
-        "classification/F1-laptop": 76.0563,
-        "classification/F1-microwave": 52.4272,
-        "classification/F1-motorcycle": 79.2982,
-        "classification/F1-mouse": 72.7273,
-        "classification/F1-orange": 61.8705,
-        "classification/F1-oven": 71.5517,
-        "classification/F1-parking meter": 56.6038,
-        "classification/F1-person": 92.0178,
-        "classification/F1-pizza": 74.6154,
-        "classification/F1-potted plant": 45.977,
-        "classification/F1-refrigerator": 62.0,
-        "classification/F1-remote": 56.7164,
-        "classification/F1-sandwich": 51.8519,
-        "classification/F1-scissors": 58.5366,
-        "classification/F1-sheep": 70.5882,
-        "classification/F1-sink": 79.5455,
-        "classification/F1-skateboard": 86.3436,
-        "classification/F1-skis": 81.4815,
-        "classification/F1-snowboard": 61.9048,
-        "classification/F1-spoon": 32.4786,
-        "classification/F1-sports ball": 53.9474,
-        "classification/F1-stop sign": 55.0336,
-        "classification/F1-suitcase": 55.7576,
-        "classification/F1-surfboard": 85.2564,
-        "classification/F1-teddy bear": 77.1084,
-        "classification/F1-tennis racket": 94.2598,
-        "classification/F1-tie": 72.4138,
-        "classification/F1-toaster": 0.0,
-        "classification/F1-toilet": 88.0734,
-        "classification/F1-toothbrush": 32.7273,
-        "classification/F1-traffic light": 61.0169,
-        "classification/F1-train": 87.0662,
-        "classification/F1-truck": 57.7006,
-        "classification/F1-tv": 72.7273,
-        "classification/F1-umbrella": 70.6294,
-        "classification/F1-vase": 62.2951,
-        "classification/F1-wine glass": 55.615,
-        "classification/F1-zebra": 96.9697,
-        "classification/Precision": 72.9971,
-        "classification/Precision-airplane": 97.7011,
-        "classification/Precision-apple": 66.0377,
-        "classification/Precision-backpack": 48.5876,
-        "classification/Precision-banana": 79.1667,
-        "classification/Precision-baseball bat": 72.8972,
-        "classification/Precision-baseball glove": 80.1802,
-        "classification/Precision-bear": 87.7551,
-        "classification/Precision-bed": 83.3333,
-        "classification/Precision-bench": 69.281,
-        "classification/Precision-bicycle": 78.5714,
-        "classification/Precision-bird": 71.2871,
-        "classification/Precision-boat": 90.2174,
-        "classification/Precision-book": 66.0494,
-        "classification/Precision-bottle": 63.6364,
-        "classification/Precision-bowl": 63.2812,
-        "classification/Precision-broccoli": 70.8861,
-        "classification/Precision-bus": 88.8889,
-        "classification/Precision-cake": 95.3846,
-        "classification/Precision-car": 57.7947,
-        "classification/Precision-carrot": 67.6923,
-        "classification/Precision-cat": 96.2687,
-        "classification/Precision-cell phone": 56.5574,
-        "classification/Precision-chair": 66.7276,
-        "classification/Precision-clock": 82.6389,
-        "classification/Precision-couch": 71.09,
-        "classification/Precision-cow": 84.3137,
-        "classification/Precision-cup": 60.472,
-        "classification/Precision-dining table": 69.4561,
-        "classification/Precision-dog": 84.6715,
-        "classification/Precision-donut": 63.3333,
-        "classification/Precision-elephant": 93.3333,
-        "classification/Precision-fire hydrant": 85.2459,
-        "classification/Precision-fork": 61.3861,
-        "classification/Precision-frisbee": 63.9175,
-        "classification/Precision-giraffe": 97.0,
+        "classification/F1-handbag": 46.9027,
+        "classification/F1-horse": 74.4589,
+        "classification/F1-hot dog": 36.1111,
+        "classification/F1-keyboard": 72.0812,
+        "classification/F1-kite": 79.2899,
+        "classification/F1-knife": 46.1538,
+        "classification/F1-laptop": 69.1489,
+        "classification/F1-microwave": 40.4255,
+        "classification/F1-motorcycle": 72.2022,
+        "classification/F1-mouse": 71.5909,
+        "classification/F1-orange": 55.7143,
+        "classification/F1-oven": 63.964,
+        "classification/F1-parking meter": 46.1538,
+        "classification/F1-person": 90.5063,
+        "classification/F1-pizza": 73.8462,
+        "classification/F1-potted plant": 46.2633,
+        "classification/F1-refrigerator": 59.5745,
+        "classification/F1-remote": 48.3333,
+        "classification/F1-sandwich": 48.0769,
+        "classification/F1-scissors": 32.4324,
+        "classification/F1-sheep": 73.6,
+        "classification/F1-sink": 72.381,
+        "classification/F1-skateboard": 79.452,
+        "classification/F1-skis": 79.0909,
+        "classification/F1-snowboard": 62.5,
+        "classification/F1-spoon": 32.2034,
+        "classification/F1-sports ball": 41.5584,
+        "classification/F1-stop sign": 61.194,
+        "classification/F1-suitcase": 45.7831,
+        "classification/F1-surfboard": 84.2105,
+        "classification/F1-teddy bear": 67.1233,
+        "classification/F1-tennis racket": 91.018,
+        "classification/F1-tie": 67.5,
+        "classification/F1-toaster": 15.3846,
+        "classification/F1-toilet": 84.7682,
+        "classification/F1-toothbrush": 22.7273,
+        "classification/F1-traffic light": 57.8313,
+        "classification/F1-train": 81.7568,
+        "classification/F1-truck": 53.9554,
+        "classification/F1-tv": 65.3595,
+        "classification/F1-umbrella": 65.5052,
+        "classification/F1-vase": 52.7607,
+        "classification/F1-wine glass": 43.3735,
+        "classification/F1-zebra": 95.858,
+        "classification/Precision": 68.6875,
+        "classification/Precision-airplane": 78.3019,
+        "classification/Precision-apple": 54.5455,
+        "classification/Precision-backpack": 39.0374,
+        "classification/Precision-banana": 62.5,
+        "classification/Precision-baseball bat": 72.3404,
+        "classification/Precision-baseball glove": 75.8621,
+        "classification/Precision-bear": 90.4762,
+        "classification/Precision-bed": 71.6667,
+        "classification/Precision-bench": 55.9006,
+        "classification/Precision-bicycle": 70.5263,
+        "classification/Precision-bird": 64.3564,
+        "classification/Precision-boat": 80.5825,
+        "classification/Precision-book": 50.7177,
+        "classification/Precision-bottle": 54.5802,
+        "classification/Precision-bowl": 60.262,
+        "classification/Precision-broccoli": 74.026,
+        "classification/Precision-bus": 79.0541,
+        "classification/Precision-cake": 81.6667,
+        "classification/Precision-car": 56.0567,
+        "classification/Precision-carrot": 51.5625,
+        "classification/Precision-cat": 93.9394,
+        "classification/Precision-cell phone": 55.5556,
+        "classification/Precision-chair": 58.4541,
+        "classification/Precision-clock": 81.9444,
+        "classification/Precision-couch": 65.3266,
+        "classification/Precision-cow": 92.8571,
+        "classification/Precision-cup": 52.514,
+        "classification/Precision-dining table": 66.5984,
+        "classification/Precision-dog": 76.8,
+        "classification/Precision-donut": 67.5,
+        "classification/Precision-elephant": 91.1111,
+        "classification/Precision-fire hydrant": 87.037,
+        "classification/Precision-fork": 46.6019,
+        "classification/Precision-frisbee": 79.7101,
+        "classification/Precision-giraffe": 95.8763,
         "classification/Precision-hair drier": 0.0,
-        "classification/Precision-handbag": 42.4403,
-        "classification/Precision-horse": 86.087,
-        "classification/Precision-hot dog": 91.6667,
-        "classification/Precision-keyboard": 84.1463,
-        "classification/Precision-kite": 92.7711,
-        "classification/Precision-knife": 47.2973,
-        "classification/Precision-laptop": 78.4884,
-        "classification/Precision-microwave": 55.102,
-        "classification/Precision-motorcycle": 89.6825,
-        "classification/Precision-mouse": 77.9221,
-        "classification/Precision-orange": 79.6296,
-        "classification/Precision-oven": 70.9402,
-        "classification/Precision-parking meter": 93.75,
-        "classification/Precision-person": 88.2674,
-        "classification/Precision-pizza": 90.6542,
-        "classification/Precision-potted plant": 67.4157,
-        "classification/Precision-refrigerator": 62.6263,
-        "classification/Precision-remote": 61.7886,
-        "classification/Precision-sandwich": 47.4576,
-        "classification/Precision-scissors": 92.3077,
-        "classification/Precision-sheep": 77.7778,
-        "classification/Precision-sink": 84.8485,
-        "classification/Precision-skateboard": 98.0,
-        "classification/Precision-skis": 91.6667,
-        "classification/Precision-snowboard": 50.6493,
-        "classification/Precision-spoon": 46.9136,
-        "classification/Precision-sports ball": 60.7407,
-        "classification/Precision-stop sign": 51.25,
-        "classification/Precision-suitcase": 76.6667,
-        "classification/Precision-surfboard": 81.5951,
-        "classification/Precision-teddy bear": 88.8889,
-        "classification/Precision-tennis racket": 95.1219,
-        "classification/Precision-tie": 96.5517,
-        "classification/Precision-toaster": 0.0,
-        "classification/Precision-toilet": 80.8989,
-        "classification/Precision-toothbrush": 42.8571,
-        "classification/Precision-traffic light": 47.6471,
-        "classification/Precision-train": 86.25,
-        "classification/Precision-truck": 63.0332,
-        "classification/Precision-tv": 68.6695,
-        "classification/Precision-umbrella": 90.1786,
-        "classification/Precision-vase": 56.5476,
-        "classification/Precision-wine glass": 67.5325,
-        "classification/Precision-zebra": 100.0,
-        "classification/Recall": 61.8844,
-        "classification/Recall-airplane": 87.6289,
-        "classification/Recall-apple": 46.0526,
-        "classification/Recall-backpack": 37.7193,
-        "classification/Recall-banana": 55.3398,
-        "classification/Recall-baseball bat": 80.4124,
-        "classification/Recall-baseball glove": 89.0,
-        "classification/Recall-bear": 87.7551,
-        "classification/Recall-bed": 70.4698,
-        "classification/Recall-bench": 45.1064,
-        "classification/Recall-bicycle": 51.6779,
-        "classification/Recall-bird": 57.6,
+        "classification/Precision-handbag": 41.1917,
+        "classification/Precision-horse": 83.4951,
+        "classification/Precision-hot dog": 61.9048,
+        "classification/Precision-keyboard": 78.022,
+        "classification/Precision-kite": 85.8974,
+        "classification/Precision-knife": 45.9016,
+        "classification/Precision-laptop": 67.3575,
+        "classification/Precision-microwave": 47.5,
+        "classification/Precision-motorcycle": 84.7458,
+        "classification/Precision-mouse": 71.5909,
+        "classification/Precision-orange": 70.9091,
+        "classification/Precision-oven": 66.3551,
+        "classification/Precision-parking meter": 80.0,
+        "classification/Precision-person": 85.9432,
+        "classification/Precision-pizza": 89.7196,
+        "classification/Precision-potted plant": 59.633,
+        "classification/Precision-refrigerator": 64.3678,
+        "classification/Precision-remote": 61.0526,
+        "classification/Precision-sandwich": 45.4545,
+        "classification/Precision-scissors": 66.6667,
+        "classification/Precision-sheep": 76.6667,
+        "classification/Precision-sink": 89.0625,
+        "classification/Precision-skateboard": 94.5652,
+        "classification/Precision-skis": 87.0,
+        "classification/Precision-snowboard": 55.5556,
+        "classification/Precision-spoon": 45.7831,
+        "classification/Precision-sports ball": 77.4194,
+        "classification/Precision-stop sign": 63.0769,
+        "classification/Precision-suitcase": 62.2951,
+        "classification/Precision-surfboard": 88.2353,
+        "classification/Precision-teddy bear": 94.2308,
+        "classification/Precision-tennis racket": 91.018,
+        "classification/Precision-tie": 85.2632,
+        "classification/Precision-toaster": 20.0,
+        "classification/Precision-toilet": 83.6601,
+        "classification/Precision-toothbrush": 50.0,
+        "classification/Precision-traffic light": 46.9055,
+        "classification/Precision-train": 87.0504,
+        "classification/Precision-truck": 54.7325,
+        "classification/Precision-tv": 59.5238,
+        "classification/Precision-umbrella": 83.1858,
+        "classification/Precision-vase": 45.5026,
+        "classification/Precision-wine glass": 64.2857,
+        "classification/Precision-zebra": 96.4286,
+        "classification/Recall": 56.3701,
+        "classification/Recall-airplane": 85.567,
+        "classification/Recall-apple": 39.4737,
+        "classification/Recall-backpack": 32.0175,
+        "classification/Recall-banana": 53.3981,
+        "classification/Recall-baseball bat": 70.1031,
+        "classification/Recall-baseball glove": 88.0,
+        "classification/Recall-bear": 77.551,
+        "classification/Recall-bed": 57.7181,
+        "classification/Recall-bench": 38.2979,
+        "classification/Recall-bicycle": 44.9664,
+        "classification/Recall-bird": 52.0,
         "classification/Recall-boat": 68.595,
-        "classification/Recall-book": 46.5217,
-        "classification/Recall-bottle": 48.0211,
-        "classification/Recall-bowl": 51.5924,
-        "classification/Recall-broccoli": 78.8732,
-        "classification/Recall-bus": 71.9577,
-        "classification/Recall-cake": 50.0,
-        "classification/Recall-car": 85.2336,
-        "classification/Recall-carrot": 54.321,
-        "classification/Recall-cat": 70.1087,
-        "classification/Recall-cell phone": 32.243,
-        "classification/Recall-chair": 62.931,
-        "classification/Recall-clock": 58.3333,
-        "classification/Recall-couch": 76.9231,
-        "classification/Recall-cow": 49.4253,
-        "classification/Recall-cup": 52.5641,
-        "classification/Recall-dining table": 66.2675,
-        "classification/Recall-dog": 65.5367,
-        "classification/Recall-donut": 61.2903,
-        "classification/Recall-elephant": 94.382,
-        "classification/Recall-fire hydrant": 60.4651,
-        "classification/Recall-fork": 40.0,
-        "classification/Recall-frisbee": 73.8095,
-        "classification/Recall-giraffe": 96.0396,
+        "classification/Recall-book": 46.087,
+        "classification/Recall-bottle": 37.7309,
+        "classification/Recall-bowl": 43.949,
+        "classification/Recall-broccoli": 80.2817,
+        "classification/Recall-bus": 61.9048,
+        "classification/Recall-cake": 39.5161,
+        "classification/Recall-car": 81.3084,
+        "classification/Recall-carrot": 40.7407,
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 }
diff --git a/focoos/model_registry/fai-cls-n-coco.json b/focoos/model_registry/fai-cls-n-coco.json
index c3b1abd1..8faa5a5f 100644
--- a/focoos/model_registry/fai-cls-n-coco.json
+++ b/focoos/model_registry/fai-cls-n-coco.json
@@ -91,21 +91,14 @@
             "use_pretrained": false,
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             "block_type": "cat",
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             "use_conv_last": false
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@@ -133,257 +126,278 @@
     "ref": null,
     "focoos_model": "fai-cls-n-coco",
     "status": "TRAINING_COMPLETED",
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 }
diff --git a/focoos/model_registry/fai-cls-s-coco.json b/focoos/model_registry/fai-cls-s-coco.json
index 17f64a40..5d59d8bb 100644
--- a/focoos/model_registry/fai-cls-s-coco.json
+++ b/focoos/model_registry/fai-cls-s-coco.json
@@ -89,23 +89,14 @@
         "num_classes": 80,
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@@ -122,7 +113,7 @@
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         "dense_prediction": false,
         "use_focal_loss": false,
@@ -139,252 +130,283 @@
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+        "classification/Precision-baseball bat": 61.0619,
+        "classification/Precision-baseball glove": 79.8077,
+        "classification/Precision-bear": 56.8966,
+        "classification/Precision-bed": 62.6168,
+        "classification/Precision-bench": 43.1579,
+        "classification/Precision-bicycle": 56.3636,
+        "classification/Precision-bird": 49.4624,
+        "classification/Precision-boat": 70.3297,
+        "classification/Precision-book": 48.7179,
+        "classification/Precision-bottle": 49.2063,
+        "classification/Precision-bowl": 53.2646,
+        "classification/Precision-broccoli": 58.1395,
+        "classification/Precision-bus": 69.8529,
+        "classification/Precision-cake": 60.7143,
+        "classification/Precision-car": 56.1747,
+        "classification/Precision-carrot": 35.3846,
+        "classification/Precision-cat": 76.9231,
+        "classification/Precision-cell phone": 39.3617,
+        "classification/Precision-chair": 55.0098,
+        "classification/Precision-clock": 75.0,
+        "classification/Precision-couch": 64.1791,
+        "classification/Precision-cow": 68.4211,
+        "classification/Precision-cup": 51.0638,
+        "classification/Precision-dining table": 55.2137,
+        "classification/Precision-dog": 71.4286,
+        "classification/Precision-donut": 38.7097,
+        "classification/Precision-elephant": 52.4138,
+        "classification/Precision-fire hydrant": 78.2609,
+        "classification/Precision-fork": 35.8974,
+        "classification/Precision-frisbee": 61.4035,
+        "classification/Precision-giraffe": 98.8636,
         "classification/Precision-hair drier": 0.0,
-        "classification/Precision-handbag": 41.1917,
-        "classification/Precision-horse": 83.4951,
-        "classification/Precision-hot dog": 61.9048,
-        "classification/Precision-keyboard": 78.022,
-        "classification/Precision-kite": 85.8974,
-        "classification/Precision-knife": 45.9016,
-        "classification/Precision-laptop": 67.3575,
-        "classification/Precision-microwave": 47.5,
-        "classification/Precision-motorcycle": 84.7458,
-        "classification/Precision-mouse": 71.5909,
-        "classification/Precision-orange": 70.9091,
-        "classification/Precision-oven": 66.3551,
-        "classification/Precision-parking meter": 80.0,
-        "classification/Precision-person": 85.9432,
-        "classification/Precision-pizza": 89.7196,
-        "classification/Precision-potted plant": 59.633,
-        "classification/Precision-refrigerator": 64.3678,
-        "classification/Precision-remote": 61.0526,
-        "classification/Precision-sandwich": 45.4545,
-        "classification/Precision-scissors": 66.6667,
-        "classification/Precision-sheep": 76.6667,
-        "classification/Precision-sink": 89.0625,
-        "classification/Precision-skateboard": 94.5652,
-        "classification/Precision-skis": 87.0,
-        "classification/Precision-snowboard": 55.5556,
-        "classification/Precision-spoon": 45.7831,
-        "classification/Precision-sports ball": 77.4194,
-        "classification/Precision-stop sign": 63.0769,
-        "classification/Precision-suitcase": 62.2951,
-        "classification/Precision-surfboard": 88.2353,
-        "classification/Precision-teddy bear": 94.2308,
-        "classification/Precision-tennis racket": 91.018,
-        "classification/Precision-tie": 85.2632,
-        "classification/Precision-toaster": 20.0,
-        "classification/Precision-toilet": 83.6601,
-        "classification/Precision-toothbrush": 50.0,
-        "classification/Precision-traffic light": 46.9055,
-        "classification/Precision-train": 87.0504,
-        "classification/Precision-truck": 54.7325,
-        "classification/Precision-tv": 59.5238,
-        "classification/Precision-umbrella": 83.1858,
-        "classification/Precision-vase": 45.5026,
-        "classification/Precision-wine glass": 64.2857,
-        "classification/Precision-zebra": 96.4286,
-        "classification/Recall": 56.3701,
-        "classification/Recall-airplane": 85.567,
-        "classification/Recall-apple": 39.4737,
-        "classification/Recall-backpack": 32.0175,
-        "classification/Recall-banana": 53.3981,
-        "classification/Recall-baseball bat": 70.1031,
-        "classification/Recall-baseball glove": 88.0,
-        "classification/Recall-bear": 77.551,
-        "classification/Recall-bed": 57.7181,
-        "classification/Recall-bench": 38.2979,
-        "classification/Recall-bicycle": 44.9664,
-        "classification/Recall-bird": 52.0,
-        "classification/Recall-boat": 68.595,
-        "classification/Recall-book": 46.087,
-        "classification/Recall-bottle": 37.7309,
-        "classification/Recall-bowl": 43.949,
-        "classification/Recall-broccoli": 80.2817,
-        "classification/Recall-bus": 61.9048,
-        "classification/Recall-cake": 39.5161,
-        "classification/Recall-car": 81.3084,
-        "classification/Recall-carrot": 40.7407,
-        "classification/Recall-cat": 67.3913,
-        "classification/Recall-cell phone": 37.3832,
-        "classification/Recall-chair": 62.5862,
-        "classification/Recall-clock": 57.8431,
-        "classification/Recall-couch": 66.6667,
-        "classification/Recall-cow": 44.8276,
-        "classification/Recall-cup": 48.2051,
-        "classification/Recall-dining table": 64.8703,
-        "classification/Recall-dog": 54.2373,
-        "classification/Recall-donut": 43.5484,
-        "classification/Recall-elephant": 92.1348,
-        "classification/Recall-fire hydrant": 54.6512,
-        "classification/Recall-fork": 30.9677,
-        "classification/Recall-frisbee": 65.4762,
-        "classification/Recall-giraffe": 92.0792,
+        "classification/Precision-handbag": 39.2226,
+        "classification/Precision-horse": 80.4348,
+        "classification/Precision-hot dog": 80.0,
+        "classification/Precision-keyboard": 74.4186,
+        "classification/Precision-kite": 83.6364,
+        "classification/Precision-knife": 42.0,
+        "classification/Precision-laptop": 54.0984,
+        "classification/Precision-microwave": 40.0,
+        "classification/Precision-motorcycle": 87.7778,
+        "classification/Precision-mouse": 70.8333,
+        "classification/Precision-orange": 46.4286,
+        "classification/Precision-oven": 63.6364,
+        "classification/Precision-parking meter": 0.0,
+        "classification/Precision-person": 87.4333,
+        "classification/Precision-pizza": 83.1683,
+        "classification/Precision-potted plant": 64.8148,
+        "classification/Precision-refrigerator": 59.7403,
+        "classification/Precision-remote": 64.1026,
+        "classification/Precision-sandwich": 38.2716,
+        "classification/Precision-scissors": 0.0,
+        "classification/Precision-sheep": 71.875,
+        "classification/Precision-sink": 79.2,
+        "classification/Precision-skateboard": 89.8305,
+        "classification/Precision-skis": 83.5165,
+        "classification/Precision-snowboard": 39.2857,
+        "classification/Precision-spoon": 36.4706,
+        "classification/Precision-sports ball": 60.8696,
+        "classification/Precision-stop sign": 68.6274,
+        "classification/Precision-suitcase": 100.0,
+        "classification/Precision-surfboard": 74.8344,
+        "classification/Precision-teddy bear": 79.4872,
+        "classification/Precision-tennis racket": 94.3089,
+        "classification/Precision-tie": 54.5455,
+        "classification/Precision-toaster": 0.0,
+        "classification/Precision-toilet": 72.5,
+        "classification/Precision-toothbrush": 0.0,
+        "classification/Precision-traffic light": 46.5201,
+        "classification/Precision-train": 84.1727,
+        "classification/Precision-truck": 48.9879,
+        "classification/Precision-tv": 53.6797,
+        "classification/Precision-umbrella": 74.359,
+        "classification/Precision-vase": 41.3979,
+        "classification/Precision-wine glass": 45.9459,
+        "classification/Precision-zebra": 93.9759,
+        "classification/Recall": 41.6596,
+        "classification/Recall-airplane": 76.2887,
+        "classification/Recall-apple": 30.2632,
+        "classification/Recall-backpack": 19.2982,
+        "classification/Recall-banana": 25.2427,
+        "classification/Recall-baseball bat": 71.134,
+        "classification/Recall-baseball glove": 83.0,
+        "classification/Recall-bear": 67.3469,
+        "classification/Recall-bed": 44.9664,
+        "classification/Recall-bench": 17.4468,
+        "classification/Recall-bicycle": 20.8054,
+        "classification/Recall-bird": 36.8,
+        "classification/Recall-boat": 52.8926,
+        "classification/Recall-book": 33.0435,
+        "classification/Recall-bottle": 32.7177,
+        "classification/Recall-bowl": 49.3631,
+        "classification/Recall-broccoli": 70.4225,
+        "classification/Recall-bus": 50.2645,
+        "classification/Recall-cake": 13.7097,
+        "classification/Recall-car": 69.7196,
+        "classification/Recall-carrot": 28.3951,
+        "classification/Recall-cat": 48.913,
+        "classification/Recall-cell phone": 17.2897,
+        "classification/Recall-chair": 48.2759,
+        "classification/Recall-clock": 47.0588,
+        "classification/Recall-couch": 44.1026,
+        "classification/Recall-cow": 14.9425,
+        "classification/Recall-cup": 43.0769,
+        "classification/Recall-dining table": 64.4711,
+        "classification/Recall-dog": 11.2994,
+        "classification/Recall-donut": 19.3548,
+        "classification/Recall-elephant": 85.3933,
+        "classification/Recall-fire hydrant": 20.9302,
+        "classification/Recall-fork": 18.0645,
+        "classification/Recall-frisbee": 41.6667,
+        "classification/Recall-giraffe": 86.1386,
         "classification/Recall-hair drier": 0.0,
-        "classification/Recall-handbag": 54.4521,
-        "classification/Recall-horse": 67.1875,
-        "classification/Recall-hot dog": 25.4902,
-        "classification/Recall-keyboard": 66.9811,
-        "classification/Recall-kite": 73.6264,
-        "classification/Recall-knife": 46.4088,
-        "classification/Recall-laptop": 71.0383,
-        "classification/Recall-microwave": 35.1852,
-        "classification/Recall-motorcycle": 62.8931,
-        "classification/Recall-mouse": 71.5909,
+        "classification/Recall-handbag": 38.0137,
+        "classification/Recall-horse": 28.9062,
+        "classification/Recall-hot dog": 7.8431,
+        "classification/Recall-keyboard": 60.3774,
+        "classification/Recall-kite": 50.5494,
+        "classification/Recall-knife": 23.2044,
+        "classification/Recall-laptop": 54.0984,
+        "classification/Recall-microwave": 18.5185,
+        "classification/Recall-motorcycle": 49.6855,
+        "classification/Recall-mouse": 57.9545,
         "classification/Recall-orange": 45.8824,
-        "classification/Recall-oven": 61.7391,
-        "classification/Recall-parking meter": 32.4324,
-        "classification/Recall-person": 95.5811,
-        "classification/Recall-pizza": 62.7451,
-        "classification/Recall-potted plant": 37.7907,
-        "classification/Recall-refrigerator": 55.4455,
-        "classification/Recall-remote": 40.0,
-        "classification/Recall-sandwich": 51.0204,
-        "classification/Recall-scissors": 21.4286,
-        "classification/Recall-sheep": 70.7692,
-        "classification/Recall-sink": 60.9626,
-        "classification/Recall-skateboard": 68.5039,
-        "classification/Recall-skis": 72.5,
-        "classification/Recall-snowboard": 71.4286,
-        "classification/Recall-spoon": 24.8366,
-        "classification/Recall-sports ball": 28.4024,
-        "classification/Recall-stop sign": 59.4203,
-        "classification/Recall-suitcase": 36.1905,
-        "classification/Recall-surfboard": 80.5369,
-        "classification/Recall-teddy bear": 52.1277,
-        "classification/Recall-tennis racket": 91.018,
-        "classification/Recall-tie": 55.8621,
-        "classification/Recall-toaster": 12.5,
-        "classification/Recall-toilet": 85.906,
-        "classification/Recall-toothbrush": 14.7059,
-        "classification/Recall-traffic light": 75.3927,
-        "classification/Recall-train": 77.0701,
-        "classification/Recall-truck": 53.2,
-        "classification/Recall-tv": 72.4638,
-        "classification/Recall-umbrella": 54.023,
-        "classification/Recall-vase": 62.7737,
-        "classification/Recall-wine glass": 32.7273,
-        "classification/Recall-zebra": 95.2941
+        "classification/Recall-oven": 42.6087,
+        "classification/Recall-parking meter": 0.0,
+        "classification/Recall-person": 91.1994,
+        "classification/Recall-pizza": 54.902,
+        "classification/Recall-potted plant": 20.3488,
+        "classification/Recall-refrigerator": 45.5446,
+        "classification/Recall-remote": 17.2414,
+        "classification/Recall-sandwich": 31.6327,
+        "classification/Recall-scissors": 0.0,
+        "classification/Recall-sheep": 35.3846,
+        "classification/Recall-sink": 52.9412,
+        "classification/Recall-skateboard": 41.7323,
+        "classification/Recall-skis": 63.3333,
+        "classification/Recall-snowboard": 67.3469,
+        "classification/Recall-spoon": 20.2614,
+        "classification/Recall-sports ball": 24.8521,
+        "classification/Recall-stop sign": 50.7246,
+        "classification/Recall-suitcase": 6.6667,
+        "classification/Recall-surfboard": 75.8389,
+        "classification/Recall-teddy bear": 32.9787,
+        "classification/Recall-tennis racket": 69.4611,
+        "classification/Recall-tie": 41.3793,
+        "classification/Recall-toaster": 0.0,
+        "classification/Recall-toilet": 77.8523,
+        "classification/Recall-toothbrush": 0.0,
+        "classification/Recall-traffic light": 66.4921,
+        "classification/Recall-train": 74.5223,
+        "classification/Recall-truck": 48.4,
+        "classification/Recall-tv": 59.9034,
+        "classification/Recall-umbrella": 16.6667,
+        "classification/Recall-vase": 56.2044,
+        "classification/Recall-wine glass": 15.4545,
+        "classification/Recall-zebra": 91.7647
     },
-    "focoos_version": "0.19.1",
-    "latency": [],
+    "focoos_version": "0.20.2",
+    "latency": [
+        {
+            "fps": 860,
+            "engine": "torchscript",
+            "min": 1.091,
+            "max": 1.3,
+            "mean": 1.163,
+            "std": 0.047,
+            "im_size": 224,
+            "device": "Tesla T4"
+        },
+        {
+            "fps": 932,
+            "engine": "onnx.CUDAExecutionProvider",
+            "min": 1.034,
+            "max": 1.303,
+            "mean": 1.072,
+            "std": 0.038,
+            "im_size": 224,
+            "device": "Tesla T4"
+        },
+        {
+            "fps": 1819,
+            "engine": "onnx.TensorrtExecutionProvider",
+            "min": 0.53,
+            "max": 0.606,
+            "mean": 0.549,
+            "std": 0.017,
+            "im_size": 224,
+            "device": "Tesla T4"
+        }
+    ],
     "training_info": null,
     "updated_at": null
 }
diff --git a/focoos/nn/backbone/convnextv2.py b/focoos/nn/backbone/convnextv2.py
index d6ace425..eb41f88c 100644
--- a/focoos/nn/backbone/convnextv2.py
+++ b/focoos/nn/backbone/convnextv2.py
@@ -158,7 +158,7 @@ def __init__(self, config: ConvNeXtV2Config):
             cur += depths[i]
 
         if config.use_pretrained and backbone_url:
-            state = torch.hub.load_state_dict_from_url(backbone_url)
+            state = torch.hub.load_state_dict_from_url(backbone_url, map_location="cpu")
             self.load_state_dict(state)
             logger.info(f"Load ConvNeXtV2{config.model_size} state_dict")
 
diff --git a/focoos/nn/backbone/csp_darknet.py b/focoos/nn/backbone/csp_darknet.py
index 399e2463..9cce322b 100644
--- a/focoos/nn/backbone/csp_darknet.py
+++ b/focoos/nn/backbone/csp_darknet.py
@@ -369,11 +369,11 @@ def __init__(
         }
         if config.use_pretrained:
             if config.backbone_url:
-                state = torch.hub.load_state_dict_from_url(config.backbone_url)
+                state = torch.hub.load_state_dict_from_url(config.backbone_url, map_location="cpu")
                 self.load_state_dict(state)
                 logger.info(f"Loaded pretrained weights from {config.backbone_url}")
             else:
-                state = torch.hub.load_state_dict_from_url(CONFIGS[config.size]["url"])
+                state = torch.hub.load_state_dict_from_url(CONFIGS[config.size]["url"], map_location="cpu")
                 self.load_state_dict(state)
                 logger.info(f"Loaded pretrained weights from {CONFIGS[config.size]['url']}")
 
diff --git a/focoos/nn/backbone/mobilenet_v2.py b/focoos/nn/backbone/mobilenet_v2.py
index c306703d..f426691a 100644
--- a/focoos/nn/backbone/mobilenet_v2.py
+++ b/focoos/nn/backbone/mobilenet_v2.py
@@ -205,7 +205,7 @@ def __init__(
             self.layers.append(layer_name)
 
         if config.use_pretrained and config.backbone_url:
-            state = torch.hub.load_state_dict_from_url(config.backbone_url)
+            state = torch.hub.load_state_dict_from_url(config.backbone_url, map_location="cpu")
             self.load_state_dict(state)
             logger.info("Load MobileNetV2 state_dict")
 
diff --git a/focoos/nn/backbone/resnet.py b/focoos/nn/backbone/resnet.py
index fff14633..c3a893ff 100644
--- a/focoos/nn/backbone/resnet.py
+++ b/focoos/nn/backbone/resnet.py
@@ -227,7 +227,7 @@ def __init__(
             self._freeze_norm(self)
 
         if use_pretrained:
-            state = torch.hub.load_state_dict_from_url(backbone_url)
+            state = torch.hub.load_state_dict_from_url(backbone_url, map_location="cpu")
             self.load_state_dict(state)
             logger.info(f"Load ResNet{depth} state_dict")
 
diff --git a/focoos/nn/backbone/stdc.py b/focoos/nn/backbone/stdc.py
index f5a958fa..e6ea0237 100644
--- a/focoos/nn/backbone/stdc.py
+++ b/focoos/nn/backbone/stdc.py
@@ -182,29 +182,51 @@ class STDCConfig(BackboneConfig):
     block_num: int = 4
     block_type: str = "cat"
     backbone_url: Optional[str] = None
-    size: Optional[Literal["small", "large"]] = None
+    size: Optional[Literal["nano", "small", "large"]] = None
     use_conv_last: bool = False
 
 
 class STDC(BaseBackbone):
     def __init__(self, config: STDCConfig):
         super().__init__(config)
-
-        if config.size == "small":
-            config.backbone_url = "https://public.focoos.ai/pretrained_models/backbones/stdc_small.pth"
-            layers = [2, 2, 2]
-            base = 64
-            block_num = 4
-            block_type = "cat"
-        elif config.size == "large":
-            config.backbone_url = "https://public.focoos.ai/pretrained_models/backbones/stdc_large.pth"
-            layers = [4, 5, 3]
-            base = 64
-            block_num = 4
-            block_type = "cat"
+        if config.size is not None:
+            if config.size == "small":
+                config.backbone_url = "https://public.focoos.ai/pretrained_models/backbones/stdc_small.pth"
+                layers = [2, 2, 2]
+                base = 64
+                block_num = 4
+                block_type = "cat"
+            elif config.size == "large":
+                config.backbone_url = "https://public.focoos.ai/pretrained_models/backbones/stdc_large.pth"
+                layers = [4, 5, 3]
+                base = 64
+                block_num = 4
+                block_type = "cat"
+            elif config.size == "nano":
+                config.backbone_url = "https://public.focoos.ai/pretrained_models/backbones/stdc_nano.pth"
+                layers = [2, 2, 2]
+                base = 32
+                block_num = 4
+                block_type = "cat"
+            else:
+                raise ValueError(f"Invalid size: {config.size}. The size should be small, large or nano.")
+            if config.layers and layers != config.layers:
+                logger.warning(f"Layers must be {layers} if size is {config.size}, provided {config.layers} not used.")
+            if config.base and base != config.base:
+                logger.warning(f"Base must be {base} if size is {config.size}, provided {config.base} not used.")
+            if config.block_num and block_num != config.block_num:
+                logger.warning(
+                    f"Block num must be {block_num} if size is {config.size}, provided {config.block_num} not used."
+                )
+            if config.block_type and block_type != config.block_type:
+                logger.warning(
+                    f"Block type must be {block_type} if size is {config.size}, provided {config.block_type} not used."
+                )
         else:
-            base = config.base
+            if not config.layers or not config.base or not config.block_num or not config.block_type:
+                raise ValueError("Layers, base, block_num and block_type must be provided if size is not provided.")
             layers = config.layers
+            base = config.base
             block_num = config.block_num
             block_type = config.block_type
 
@@ -226,7 +248,7 @@ def __init__(self, config: STDCConfig):
             self.out_ids = 1, 5, 10, 13
 
         if config.use_pretrained and config.backbone_url:
-            state = torch.hub.load_state_dict_from_url(config.backbone_url)
+            state = torch.hub.load_state_dict_from_url(config.backbone_url, map_location="cpu")
             self.load_state_dict(state)
             logger.info("Load STDC state_dict")
 
diff --git a/focoos/nn/backbone/swin.py b/focoos/nn/backbone/swin.py
index bbce7f6e..46cafc7a 100644
--- a/focoos/nn/backbone/swin.py
+++ b/focoos/nn/backbone/swin.py
@@ -679,7 +679,7 @@ def __init__(
             self.add_module(layer_name, layer)
 
         if config.use_pretrained and backbone_url:
-            state = torch.hub.load_state_dict_from_url(backbone_url)
+            state = torch.hub.load_state_dict_from_url(backbone_url, map_location="cpu")
             self.load_state_dict(state, strict=False)
             logger.info(f"Loaded pretrained weights from {backbone_url}")
 
diff --git a/tests/test_backbone.py b/tests/test_backbone.py
index ddc967f2..12093d42 100644
--- a/tests/test_backbone.py
+++ b/tests/test_backbone.py
@@ -6,14 +6,54 @@
 from focoos.nn.backbone.build import load_backbone
 
 # List of all backbone types with their minimum required config
+
+stdc_configs_size = [
+    {"model_type": "stdc", "use_pretrained": True, "size": size} for size in ["nano", "small", "large"]
+]
+stdc_configs_base = [
+    {
+        "model_type": "stdc",
+        "use_pretrained": True,
+        "base": 64,
+        "layers": [2, 2, 2],
+        "block_num": 4,
+        "block_type": "cat",
+    },
+    {
+        "model_type": "stdc",
+        "use_pretrained": False,
+        "base": 64,
+        "layers": [4, 5, 3],
+        "block_num": 4,
+        "block_type": "cat",
+    },
+]
 BACKBONE_CONFIGS = {
-    "resnet": [{"model_type": "resnet", "use_pretrained": False, "depth": 18}],
-    "stdc": [{"model_type": "stdc", "use_pretrained": False, "base": 64, "layers": [4, 5, 3]}],
-    "swin": [{"model_type": "swin", "use_pretrained": False}],
-    "mobilenet_v2": [{"model_type": "mobilenet_v2", "use_pretrained": False}],
-    "convnextv2": [{"model_type": "convnextv2", "use_pretrained": False}],
+    "resnet": [
+        {"model_type": "resnet", "use_pretrained": True, "depth": 18},
+        {"model_type": "resnet", "use_pretrained": True, "depth": 34},
+        {"model_type": "resnet", "use_pretrained": False, "depth": 50},
+        {"model_type": "resnet", "use_pretrained": False, "depth": 101},
+    ],
+    "stdc": stdc_configs_size + stdc_configs_base,
+    "swin": [
+        {"model_type": "swin", "use_pretrained": False},
+        {
+            "model_type": "swin",
+            "use_pretrained": True,
+        },
+    ],
+    "mobilenet_v2": [
+        {"model_type": "mobilenet_v2", "use_pretrained": False},
+        {"model_type": "mobilenet_v2", "use_pretrained": True},
+    ],
+    "convnextv2": [
+        {"model_type": "convnextv2", "use_pretrained": False},
+        {"model_type": "convnextv2", "use_pretrained": True},
+    ],
     "csp_darknet": [
-        {"model_type": "csp_darknet", "use_pretrained": False, "size": size} for size in ["small", "medium", "large"]
+        {"model_type": "csp_darknet", "use_pretrained": True if size == "small" else False, "size": size}
+        for size in ["small", "medium", "large"]
     ],
 }
 
diff --git a/tutorials/quantize_classification.ipynb b/tutorials/quantize_classification.ipynb
index 7618f7fb..8cc4475b 100644
--- a/tutorials/quantize_classification.ipynb
+++ b/tutorials/quantize_classification.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "Focoos offers three pretrained classification models in different sizes:\n",
     "\n",
-    "- fai-cls-n-coco  (nano) \n",
+    "- fai-cls-n-coco  (nano, optimized for Arduino Nicla Vision) \n",
     "- fai-cls-s-coco  (small)\n",
     "- fai-cls-m-coco  (medium)\n",
     "\n",
@@ -37,7 +37,7 @@
     "\n",
     "from focoos import ModelManager\n",
     "\n",
-    "model_name = \"fai-cls-m-coco\"  # you can also take model from focoos hub with \"hub://YOUR_MODEL_REF\"\n",
+    "model_name = \"fai-cls-n-coco\"  # you can also take model from focoos hub with \"hub://YOUR_MODEL_REF\"\n",
     "\n",
     "model = ModelManager.get(model_name)\n",
     "pprint(model.model_info)"
@@ -66,13 +66,13 @@
     "\n",
     "from focoos import ASSETS_DIR, MODELS_DIR, RuntimeType\n",
     "\n",
-    "image_size = 224  # 224px input size\n",
+    "image_size = 96  # 96px input size\n",
     "\n",
     "exported_model = model.export(\n",
     "    runtime_type=RuntimeType.ONNX_CPU,  # optimized for edge or cpu\n",
     "    image_size=image_size,\n",
     "    dynamic_axes=False,  # quantization need static axes!\n",
-    "    simplify_onnx=False,  # simplify and optimize onnx model graph\n",
+    "    simplify_onnx=True,  # simplify and optimize onnx model graph\n",
     "    onnx_opset=18,\n",
     "    out_dir=os.path.join(MODELS_DIR, \"my_edge_model\"),\n",
     ")  # save to models dir\n",
@@ -108,9 +108,9 @@
     "    calibration_images_folder=str(ASSETS_DIR),  # Calibration images folder: It is strongly recommended\n",
     "    # to use the dataset validation split on which the model was trained.\n",
     "    # Here, for example, we will use the assets folder.\n",
-    "    format=\"QDQ\",  # QO (QOperator): All the quantized operators have their own ONNX definitions, like QLinearConv, MatMulInteger etc.\n",
+    "    format=\"QO\",  # QO (QOperator): All the quantized operators have their own ONNX definitions, like QLinearConv, MatMulInteger etc.\n",
     "    # QDQ (Quantize-DeQuantize): inserts DeQuantizeLinear(QuantizeLinear(tensor)) between the original operators to simulate the quantization and dequantization process.\n",
-    "    per_channel=True,  # Per-channel quantization: each channel has its own scale/zero-point → more accurate,\n",
+    "    per_channel=False,  # Per-channel quantization: each channel has its own scale/zero-point → more accurate,\n",
     "    # especially for convolutions, at the cost of extra memory and computation.\n",
     "    normalize_images=True,  # normalize images during preprocessing: some models have normalization outside of model forward\n",
     ")\n",
diff --git a/uv.lock b/uv.lock
index a6bc08aa..852ee1bc 100644
--- a/uv.lock
+++ b/uv.lock
@@ -677,7 +677,7 @@ wheels = [
 
 [[package]]
 name = "focoos"
-version = "0.19.1"
+version = "0.20.2"
 source = { editable = "." }
 dependencies = [
     { name = "colorama" },