PyDetect8-Human-Detection-Reimagined : YOLOv8 re-implementation for person detection using PyTorch

AUTHOR : MANAI MORTADHA

Description

This repository contains a re-implementation of YOLOv8 specifically designed for person detection using PyTorch. It provides a streamlined approach to implement and utilize YOLOv8 for detecting individuals within images.

Installation

conda create -n YOLO python=3.8
conda activate YOLO
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
pip install opencv-python==4.5.5.64
pip install PyYAML
pip install tqdm

Train

• Configure your dataset path in main.py for training
• Run bash main.sh $ --train for training, $ is number of GPUs

Test

• Configure your dataset path in main.py for testing
• Run python main.py --test for testing

Results

Version	COCO weights	CrowdHuman weights
v8_n	model	model
v8_s	model	-
v8_m	model	-
v8_l	model	-
v8_x	model	-

• the weights are ported from original repo, see reference

Dataset structure

├── Person 
    ├── images
        ├── train2017
            ├── 1111.jpg
            ├── 2222.jpg
        ├── val2017
            ├── 1111.jpg
            ├── 2222.jpg
    ├── labels
        ├── train2017
            ├── 1111.txt
            ├── 2222.txt
        ├── val2017
            ├── 1111.txt
            ├── 2222.txt

• Public person detection datasets
  • COCO
  • CrowdHuman
  • HIEVE
  • VisDrone
  • VFP290K
  • Argoverse
  • CEPDOF
  • HABBOF
  • MW-R
  • TIDOS
  • WEPDTOF
  • DEPOF
  • FRIDA

Reference

• https://github.com/ultralytics/yolov5
• https://github.com/ultralytics/ultralytics
• https://github.com/open-mmlab/mmyolo

PyDetect8-Human-Detection-Reimagined