Year
2024
Role
Full implementation — dataset, loss function, attention module, training
Stack
- PyTorch
- YOLOv1
- CBAM
- ReduceLROnPlateau
Architecture study
YOLOv1 With CBAM From Scratch
YOLOv1 rebuilt from scratch in PyTorch, with its own dataset class and loss function, and CBAM attention added to the feature extractor.
- This project rebuilt the YOLOv1 model from scratch using PyTorch, where I implemented FruitDataset and YoloLoss, and integrated CBAM (Convolutional Block Attention Module) to enhance feature extraction, with a total of 269,168,434 parameters.
- The model employed the ReduceLROnPlateau scheduler to optimize the training process.
- Training results showed a Train mAP of 0.8829 and a Best Validation mAP of 0.6994.
- These results demonstrate that CBAM provides significant improvements in object detection performance.
- Train mAP
- 0.8829
- Best val mAP
- 0.6994
- Parameters
- 269.2M
- 269,168,434
