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.