README.md

A personal implementation of YOLOv5(m)

This repository is thought to facilitate the understanding of YOLOv5 by creating a simple file structure focused on the PyTorch implementation. The sources I used while implementating are Ultralytics YOLOv5 for architecture details and Aladdin Persson YOLOv3 for the training-pipeline design.

Instruction for use:

git clone https://github.com/AlessandroMondin/YOLOV5m.git
cd YOLOv5m
pip install -r requirements.txt

To run experiments, your datasets need to comply with the below tree structure and the corresponing folder names.

yolov5m (this repository)
datasets
└── YOUR_DATASET
    ├── images
    │   ├── train
    │   └── val
    └── labels
        ├── train
        └── val

After loading Ultralytics COCO weights in my architecture, I've fine-tuned it on FLIR dataset for ~15 epoch and it reached ~0.82 of MAP50