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-# swarm
-Machine learning tooling and scripts for Hive.
+# Swarm Machine-Learning Toolkit
 
-## Components
+Swarm packages the data-cleaning pipeline, annotation helpers, and training scripts that CU Robotics uses to build perception models for Hive. The repository is organized around four main areas: cleaning raw collections, preparing datasets, training/evaluating models, and running exploratory notebooks.
 
-* `/cleaning`
+## Prerequisites
+- Python 3.11+ with `venv`.
+- System packages required by OpenCV’s GUI backends (for example, `sudo apt install libgl1` on Ubuntu).
+- A PyTorch build appropriate for your CPU/GPU (install separately via <https://pytorch.org/get-started/locally/>).
 
-  Data cleaning and labeling pipeline utilities. View `/cleaning/README.md` for pipeline documentation.
+## Environment Setup
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+# install PyTorch separately for your hardware, e.g.
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+```
+
+## Working with Collections
+Collections live under `collections/<name>/` and typically contain:
+- `calibrations/` — camera YAML files used by undistortion stages.
+- `examples/` — `raw.tar.gz` plus an optional `pipeline-root.json` that seeds metadata.
+- Pipeline outputs produced by the scripts below.
+
+Run the default cleaning pipeline (destroys and rebuilds transient outputs each time):
+```bash
+cd cleaning
+./default_pipeline.sh /abs/path/to/collection/examples -l INFO
+```
+You can execute individual stages with `./pipeline.py` (see inline help for options) and reset outputs using `python destroy_pipeline_state.py`. Stage implementations live in `cleaning/*.py`; the Flask bounding-box annotator is served by `label_bbox.py`.
+
+## Building Datasets and Training
+```bash
+cd cnn
+python combine_metadata.py ../collections/<dataset>
+python clean_metadata.py ../collections/<dataset>
+python train_model.py ../collections/<dataset>
+python test_model.py ../collections/<dataset>
+```
+- `combine_metadata.py` aggregates metadata from cleaned batches.
+- `clean_metadata.py` discards rows that reference missing files.
+- `train_model.py` instantiates the ConvNet defined in `ConvNet.py` and saves checkpoints.
+- `test_model.py` evaluates the saved weights and reports accuracy/misclassifications.
+
+`run_model.py` offers a real-time inference demo against a connected camera (`/dev/stereo-cam-right-video` by default). Adjust device paths or color thresholds as needed.
+
+## Notebooks
+Exploratory analyses live under `jupyter/` (for example, `inspect_plates.ipynb` for cropped plates, `undistort.ipynb` for calibration experiments). Activate the virtual environment before launching Jupyter.
+
+## Contribution Checklist
+- Keep pipeline contracts stable—coordinate schema or tooling changes with the ML tooling maintainers before merging.
+- Document new stages or scripts directly in code docstrings or a short note in `cleaning/README.md`.
+- Capture representative metrics or plots (training curves, confusion matrices, etc.) when submitting model or pipeline changes.
+- Run relevant scripts end-to-end (cleaning + training) and include the commands plus key results in your PR description.