This repository contains the code for HIC-YOLOv5, an improved version of YOLOv5 tailored for small object detection. The improvements are based on the paper HIC-YOLOv5: Improved YOLOv5 For Small Object Detection.
HIC-YOLOv5 incorporates Channel Attention Block (CBAM) and Involution modules for enhanced object detection, making it suitable for both CPU and GPU training.
The installation process for HIC-YOLOv5 is identical to the YOLOv5 repository. You can follow the installation instructions provided in the YOLOv5 GitHub repository.
To use HIC-YOLOv5, you can specify the configuration file with the --cfg argument. An example command for training might look like this:
python train.py --img-size 640 --batch 16 --epochs 100 --data data/coco.yaml --cfg models/yolo5m-cbam-involution.yaml--img-size: Specifies the input image size.--batch: Sets the batch size for training.--epochs: Defines the number of training epochs.--data: Specifies the data configuration file.--cfg: Points to the configuration file for HIC-YOLOv5. In this case, it's themodels/yolo5m-cbam-involution.yaml.
I am actively working on adding support for multi-GPU training. Please stay tuned for updates on testing and training with multiple GPUs.
I want to express our gratitude to the authors of the paper "HIC-YOLOv5: Improved YOLOv5 For Small Object Detection" for their contributions, which inspired the development of HIC-YOLOv5.
HIC-YOLOv5 is released under the MIT License. Please refer to the LICENSE file for more details.
For additional information and updates, please refer to the YOLOv5 GitHub repository.
Note: Be sure to refer to the official YOLOv5 repository for the latest updates and documentation.