README.md

Men-wome-detection-using-yolov8

This guide will provide instructions on how to convert OIDv4 data into the YOLO format for use with YOLOv8 object detection algorithms.

Getting Started

git clone https://github.com/prince0310/Men-wome-detection-using-yolov8-.git

Dataset

For training custom data set on yolo model you need to have data set arrangement in yolo format. which includes Images and Their annotation file.

clone the repository and run donload the data set and their annotation file

git clone https://github.com/prince0310/OIDv4_ToolKit.git

Implement convert annotation.ipynb notebook

it will create data in below format

Custom dataset
        |
        |─── train
        |    |
        |    └───Images --- 0fdea8a716155a8e.jpg
        |    └───Labels --- 0fdea8a716155a8e.txt
        |
        └─── test
        |    └───Images --- 0b6f22bf3b586889.jpg
        |    └───Labels --- 0b6f22bf3b586889.txt
        |
        └─── validation
        |    └───Images --- 0fdea8a716155a8e.jpg
        |    └───Labels --- 0fdea8a716155a8e.txt
        |
        └─── data.yaml

Install

Pip install the ultralytics package including all requirements.txt in a 3.10>=Python>=3.7 environment, including PyTorch>=1.7.

pip install ultralytics

Train

Python

from ultralytics import YOLO

# Train
model = YOLO("yolov8n.pt")

results = model.train(data="data.yaml", epochs=200, workers=1, batch=8,imgsz=640)  # train the model

Cli

yolo detect train data=data.yaml model=yolov8n.pt epochs=200 imgsz=640

Detect

Python

from ultralytics import YOLO

# Load a model
model = YOLO("best.pt")  # load a custom model

# Predict with the model
results = model("image.jpg", save = True)  # predict on an image

Cli

yolo detect predict model=path/to/best.pt source="images.jpg"  # predict with custom model