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RESUME_TRAINING_20_EPOCHS.md

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+# Resume Training from 20-Epoch Checkpoint
+
+## Checkpoint Information
+
+**Checkpoint File:** `/workspace/soccer_cv_ball/models/soccer ball/checkpoint_20_soccer_ball.pth`  
+**Size:** 474.57 MB  
+**Epoch:** 19 (completed 20 epochs, 0-19)  
+**Next Epoch:** 20
+
+## Training Configuration
+
+### Model Architecture
+- **Model Type:** RF-DETR Base
+- **Encoder:** dinov2_windowed_small
+- **Resolution:** 1288x1288
+- **Classes:** 2 (ball + background)
+- **Class Names:** ['ball']
+- **Num Queries:** 300
+- **Decoder Layers:** 3
+- **Hidden Dim:** 256
+- **Self-Attention Heads:** 8
+- **Cross-Attention Heads:** 16
+
+### Training Hyperparameters
+- **Batch Size:** 2
+- **Gradient Accumulation Steps:** 16 (effective batch size: 32)
+- **Learning Rate:** 0.0002
+- **Encoder Learning Rate:** 0.00015
+- **Weight Decay:** 0.0001
+- **Gradient Clip:** 0.1
+- **Total Epochs:** 20
+- **Warmup Epochs:** 0.0
+- **LR Scheduler:** step
+- **LR Drop:** 100 (not reached)
+- **Mixed Precision (AMP):** Enabled
+
+### Loss Configuration
+- **Classification Loss Coef:** 1.0
+- **Bbox Loss Coef:** 5
+- **GIoU Loss Coef:** 2
+- **Focal Alpha:** 0.25
+- **Auxiliary Loss:** Enabled
+- **Set Cost Class:** 2
+- **Set Cost Bbox:** 5
+- **Set Cost GIoU:** 2
+
+### Optimizer & Scheduler
+- **Optimizer State:** ✅ Saved in checkpoint
+- **Scheduler State:** ✅ Saved in checkpoint
+- **EMA Model:** ✅ Saved (decay: 0.993, tau: 100)
+
+### Dataset Information
+- **Original Dataset Path:** `/workspace/soccer_coach_cv/models/ball_detection_open_soccer_ball/dataset`
+- **Dataset Format:** Roboflow (YOLO converted to COCO)
+- **Original Output Dir:** `/workspace/soccer_coach_cv/models/ball_detection_open_soccer_ball`
+
+### Checkpoint Contents
+- ✅ Model state dict (487 layers)
+- ✅ Optimizer state dict
+- ✅ Learning rate scheduler state
+- ✅ EMA model state
+- ✅ Training arguments
+- ✅ Epoch number (19)
+
+## How to Resume Training
+
+### Option 1: Using the Resume Script
+
+```bash
+cd /workspace/soccer_cv_ball
+python scripts/resume_from_20_epochs.sh
+```
+
+### Option 2: Using train_ball.py with Resume Flag
+
+First, update the dataset path in the config or script to match your current dataset location, then:
+
+```bash
+cd /workspace/soccer_cv_ball
+python scripts/train_ball.py \
+    --config configs/resume_20_epochs.yaml \
+    --output-dir models
+```
+
+### Option 3: Direct RF-DETR Training
+
+If using RF-DETR directly:
+
+```python
+from rfdetr import RFDETRBase
+
+# Initialize model
+model = RFDETRBase(class_names=['ball'])
+
+# Load checkpoint
+checkpoint_path = "/workspace/soccer_cv_ball/models/soccer ball/checkpoint_20_soccer_ball.pth"
+checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
+
+# Load model weights
+if 'model' in checkpoint:
+    model_state = checkpoint['model']
+    if hasattr(model, 'model') and hasattr(model.model, 'model'):
+        current_state = model.model.model.state_dict()
+        filtered_state = {}
+        for key, value in model_state.items():
+            if key in current_state and current_state[key].shape == value.shape:
+                filtered_state[key] = value
+        model.model.model.load_state_dict(filtered_state, strict=False)
+
+# Continue training with RF-DETR's train() method
+# (pass resume=checkpoint_path to resume from epoch 20)
+```
+
+## Important Notes
+
+1. **Dataset Path:** The original training used a dataset at `/workspace/soccer_coach_cv/models/ball_detection_open_soccer_ball/dataset`. You may need to:
+   - Update the dataset path in the config/script to match your current dataset location
+   - Or ensure the dataset exists at the original path
+
+2. **Epoch Continuation:** The checkpoint is at epoch 19, so resuming will start from epoch 20. If you want to train for more epochs, update the `epochs` parameter.
+
+3. **Output Directory:** The original training saved to `/workspace/soccer_coach_cv/models/ball_detection_open_soccer_ball`. You may want to change this to save in the current workspace.
+
+4. **Model Compatibility:** The checkpoint uses RF-DETR format with the model structure: `model.model.model` (RFDETRBase -> Model -> LWDETR).
+
+## Files Created
+
+1. **`training_info_20_epochs.json`** - Complete training information extracted from checkpoint
+2. **`configs/resume_20_epochs.yaml`** - YAML config for resuming training
+3. **`scripts/resume_from_20_epochs.sh`** - Python script to resume training
+4. **`scripts/verify_checkpoint.py`** - Script to verify checkpoint validity
+
+## Training Progress
+
+- **Completed:** 20 epochs (0-19)
+- **Checkpoint saved:** Epoch 19
+- **Ready to resume:** Yes ✅
+
+## Next Steps
+
+1. Verify dataset path exists and is accessible
+2. Update paths in config/script if needed
+3. Run resume script to continue training from epoch 20
+4. Monitor training logs and metrics