MnLgt / yolo-human-parse

YOLO Segmentation Model for Human Body Parts and Objects

This repository contains a fine-tuned YOLO (You Only Look Once) segmentation model designed to detect and segment various human body parts and objects in images.

Model Overview

The model is based on the YOLO architecture and has been fine-tuned to detect and segment the following classes:

1. Hair
2. Face
3. Neck
4. Arm
5. Hand
6. Back
7. Leg
8. Foot
9. Outfit
10. Person
11. Phone

Installation

To use this model, you'll need to have the appropriate YOLO framework installed. Please follow these steps:

1. Clone this repository:

   git clone https://github.com/your-username/yolo-segmentation-human-parts.git
   cd yolo-segmentation-human-parts
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

To use the model for inference, you can use the following Python script:

from ultralytics import YOLO

# Load the model
model = YOLO('path/to/your/model.pt')

# Perform inference on an image
results = model('path/to/your/image.jpg')

# Process the results
for result in results:
    boxes = result.boxes  # Bounding boxes
    masks = result.masks  # Segmentation masks
    # Further processing...

Training

If you want to further fine-tune the model on your own dataset, please follow these steps:

1. Prepare your dataset in the YOLO format.
2. Modify the data.yaml file to reflect your dataset structure and classes.
3. Run the training script:

   python train.py --img 640 --batch 16 --epochs 100 --data data.yaml --weights yolov5s-seg.pt
   

Evaluation

To evaluate the model's performance on your test set, use:

python val.py --weights path/to/your/model.pt --data data.yaml --task segment

Contributing

Contributions to improve the model or extend its capabilities are welcome. Please submit a pull request or open an issue to discuss proposed changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the YOLO team for the original implementation.
• Gratitude to all contributors who helped in fine-tuning and improving this model.