Yolo-human-parse Open-source Visual Segmentation Model - Accurately Detect and Segment Human Body Parts and Common Objects in Images

Yolo Human Parse

Developed by MnLgt

A vision segmentation model based on the YOLO architecture, specifically designed to detect and segment human body parts and common objects in images.

Image Segmentation

Transformers

Open Source License:Apache-2.0 #Human body part segmentation #Multi-object detection #Real-time segmentation

Downloads 20

Release Time : 5/8/2024

Model Overview

This fine-tuned model accurately identifies and segments 11 categories in images, including human body parts such as hair, face, hands, and objects like mobile phones. Suitable for applications requiring detailed human body part analysis.

Model Features

Multi-category fine segmentation

Capable of detecting and segmenting 11 different categories simultaneously, including various human body parts and common objects.

Based on YOLO architecture

Utilizes the YOLO real-time object detection framework, balancing speed and accuracy.

Easy to fine-tune

Provides complete training and evaluation scripts, supporting users in fine-tuning the model with custom datasets.

Model Capabilities

Human body part detection

Object detection

Image segmentation

Real-time analysis

Use Cases

Fashion and Retail

Virtual try-on

Achieves virtual try-on effects by precisely segmenting human body and clothing parts.

Human-Computer Interaction

Gesture recognition

Enables precise gesture recognition through hand segmentation.

Health and Fitness

Exercise posture analysis

Analyzes exercise postures by segmenting limb parts.

🚀 YOLO Segmentation Model for Human Body Parts and Objects

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

🚀 Quick Start

This repository contains a fine - tuned YOLO segmentation model for detecting and segmenting human body parts and objects in images.

✨ Features

The model is based on the YOLO architecture and has been fine - tuned to detect and segment the following classes: 0. Hair

Face
Neck
Arm
Hand
Back
Leg
Foot
Outfit
Person
Phone

📦 Installation

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

Clone this repository:

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

Install the required dependencies:
```
pip install -r requirements.txt
```

💻 Usage Examples

Basic Usage

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...

📚 Documentation

Training

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

Prepare your dataset in the YOLO format.
Modify the data.yaml file to reflect your dataset structure and classes.

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.

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