removed gradio

2025-12-23 18:24:40 -07:00
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+# Training and Deployment Scripts
+
+This directory contains separate scripts for training models and converting them for deployment, extracted from the annotation GUI.
+
+## Training Script
+
+### `train_model.py`
+
+Train object detection models for wood knot detection.
+
+**Supported frameworks:**
+- RF-DETR (MIT license)
+- RT-DETR (Apache 2.0 license)
+- YOLOv6 (MIT license)
+- YOLOX (MIT license)
+
+**Usage:**
+
+```bash
+# Basic usage
+python train_model.py --framework rtdetr --dataset dataset_prepared --output runs/training
+
+# Full options
+python train_model.py \
+    --framework rtdetr \
+    --dataset dataset_prepared \
+    --output runs/training \
+    --model-size small \
+    --epochs 20 \
+    --batch-size 4 \
+    --lr 0.001 \
+    --prepare-dataset \
+    --images-dir IMAGE \
+    --annotations annotations.json
+```
+
+**Options:**
+- `--framework`: Model framework (rf-detr, rt-detr, yolov6, yolox)
+- `--dataset`: Path to prepared dataset directory
+- `--output`: Output directory for trained model
+- `--model-size`: Model size/variant (nano, small, medium, base)
+- `--epochs`: Number of training epochs
+- `--batch-size`: Batch size for training
+- `--lr`: Learning rate
+- `--prepare-dataset`: Prepare dataset from annotations first
+- `--images-dir`: Images directory (for --prepare-dataset)
+- `--annotations`: Annotations file (for --prepare-dataset)
+
+## Deployment Conversion Script
+
+### `convert_for_deployment.py`
+
+Convert trained models for OAK-D deployment.
+
+**Supported conversions:**
+- ONNX export
+- OpenVINO IR export
+- Model optimization for edge devices
+
+**Usage:**
+
+```bash
+# Basic usage
+python convert_for_deployment.py --model runs/training/weights/best.pt --output oak_d_deployment
+
+# Full options
+python convert_for_deployment.py \
+    --model runs/training/weights/best.pt \
+    --output oak_d_deployment \
+    --img-size 640 \
+    --framework auto
+```
+
+**Options:**
+- `--model`: Path to trained model weights (.pt file)
+- `--output`: Output directory for converted models
+- `--img-size`: Input image size for the model (320, 416, 512, 640, 800, 1024)
+- `--framework`: Model framework (auto-detect if not specified)
+
+## Workflow
+
+1. **Annotate images** using the Tkinter GUI (`./run_tk_gui.sh` or `python tk_annotation_gui.py`)
+2. **Export annotations** to COCO format from the GUI
+3. **Prepare dataset** (optional, can be done by training script):
+   ```bash
+   python train_model.py --prepare-dataset --images-dir IMAGE --annotations annotations.json --dataset dataset_prepared
+   ```
+4. **Train model**:
+   ```bash
+   python train_model.py --framework rtdetr --dataset dataset_prepared --output runs/training
+   ```
+5. **Convert for deployment**:
+   ```bash
+   python convert_for_deployment.py --model runs/training/weights/best.pt --output oak_d_deployment
+   ```
+
+## Next Steps After Conversion
+
+After running `convert_for_deployment.py`:
+
+1. **Test OpenVINO Model** (optional):
+   ```bash
+   python -c "from openvino.runtime import Core; core = Core(); model = core.read_model('model.xml'); print('✓ Model loaded')"
+   ```
+
+2. **Convert to RVC compiled format** (recommended by Luxonis):
+   - Online: HubAI conversion (fastest setup)
+   - Offline: ModelConverter (requires Docker)
+   - Docs: https://docs.luxonis.com/software-v3/ai-inference/conversion/
+
+3. **Deploy to OAK-D**:
+   - Use DepthAI Python API
+   - Or use OAK-D examples with your blob
+
+## Tips
+
+- **Nano models** work best on edge devices
+- If you quantize, use real calibration images for best accuracy
+- Test inference speed vs accuracy trade-off
+- All models are MIT/Apache 2.0 licensed - free for commercial use!
+<parameter name="filePath">/home/dillon/_code/saw_mill_knot_detection/TRAINING_README.md