Skip to content

Latest commit

 

History

History
27 lines (21 loc) · 1.63 KB

README.md

File metadata and controls

27 lines (21 loc) · 1.63 KB
Raw

BranchyYOLO

A Deep Learning model built using the same idea as YOLO, but with a 2-branch topology. This is its PyTorch implementation for Risiko! table game's pieces object detection.

In this repository there is also an ablated version of YOLOv9-C which has been studied during the development of BranchyYOLO.

For more information read the report.

Requirements

  • PyTorch
  • YOLOv9-C official implementation: git clone 'https://github.com/WongKinYiu/yolov9.git' After downloading it run the following commands:
    • sed -i 's/opt.min_items/min_items/' yolov9/val.py
    • sed -i 's/opt.min_items/min_items/' yolov9/val_dual.py
  • Install the yolov9 requirements: pip install -r yolov9/requirements.txt

Files explanation

We don't include our training dataset because it is too big

  • The file run.py contains the code to train the model and also to test it

    Important: modify run.py imports according to the model being trained: use *_dual files if and only if the ablated model is going to be used (since it has the DualDetect block instead of the Detect block at the end).

  • The file BranchyYOLO.yaml contains the definition of BranchyYOLO model; it will be imported by models.yolo.parse_model
  • The file AblatedYOLOv9-C.yaml contains the definition of the ablated version of YOLOv9-C
  • The file hyp.yaml contains the definition of some hyperparameters used during the training phase
  • The file coco.yaml contains the definition of the dataset used for training, validation and testing
  • The file Detection.ipynb can be used to perform object detection in images