dsabarinathan / attribute-recognition

This repository contains a PyTorch implementation of a person attribute recognition model. The model has been trained to recognize various attributes such as age, gender, hair length, upper body features, lower body features, and accessories.

Person Attribute Recognition with Deep Learning

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Overview

This repository contains a PyTorch implementation of a person attribute recognition model. The model has been trained to recognize various attributes such as age, gender, hair length, upper body features, lower body features, and accessories.

Pre-trained model

Model	ROC AUC	F1 Score	Model Type
Resnet 18	0.9221371	0.910283516	Pytorch
Resnet 30	0.94394	0.943229	Pytorch

Model Details

The model is trained to recognize the following attributes:

• Age

  • Young
  • Adult
  • Old

• Gender

  • Female

• Hair Length

  • Short
  • Long
  • Bald

• Upper Body Features

  • Length
    • Short
  • Color
    • Black
    • Blue
    • Brown
    • Green
    • Grey
    • Orange
    • Pink
    • Purple
    • Red
    • White
    • Yellow
    • Other

• Lower Body Features

  • Length
    • Short
  • Color
    • Black
    • Blue
    • Brown
    • Green
    • Grey
    • Orange
    • Pink
    • Purple
    • Red
    • White
    • Yellow
    • Other
  • Type
    • Trousers & Shorts
    • Skirt & Dress

• Accessories

  • Backpack
  • Bag
  • Glasses
    • Normal
    • Sun
  • Hat

Usage

1. Installation

Clone the repository and navigate to the project directory:

git clone https://github.com/dsabarinathan/attribute-recognition.git
cd attribute-recognition

2. Download Pre-trained Model

Download the pre-trained model weights file from the releases section of this repository and place it in the models/ directory.

The pre-trained model weights can be downloaded from Google Drive. Download Model

3. Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

4. Run Inference

Use the provided script to perform attribute recognition on an input image:

python inference.py --image_path path/to/your/image.jpg

Replace path/to/your/image.jpg with the path to the image you want to analyze.

5. Sample Results:

Input image:

Output:

Predicted results: {'labels': array(['Age-Adult', 'Gender-Female', 'LowerBody-Color-Black',
       'LowerBody-Type-Trousers&Shorts'], dtype='<U30'), 'prob': array([0.64786081, 0.61053316, 0.63944295, 0.85024354])}

Contributing

We welcome contributions! If you find any issues or have suggestions for improvements, please create an issue or submit a pull request.

License

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

attribute-recognition's Issues

Train script

Can you provide some information like Dataset and how did you train