Transformers

Project Description

The project aims to provide an easy-to-use implementation of the DETR (DEtection TRansformers) model for object detection and segmentation. DETR is a transformer-based model that has shown promising results in these computer vision tasks.

Files

• detr.py: This file contains the main implementation of the DETR model, including the transformer architecture for object detection.
• backbone.py: Implements the backbone network used in the DETR model.
• matcher.py: Provides the matching mechanism used to associate objects in the query and target sets.
• position_encoding.py: Contains the positional encoding mechanism used in the transformer.
• segmentation.py: Implements the segmentation head of the DETR model.
• transformer.py: Defines the core transformer blocks used in the model.

Usage

To use the DETR model for object detection and segmentation, you can follow these steps:

1. Initialize the model with your desired configuration.
2. Load pre-trained weights if available.
3. Provide input images for inference.
4. Run the model for object detection and segmentation.

Here's an example Python script that demonstrates the basic usage:

from detr import DETR

# Initialize the model
model = DETR()

# Load pre-trained weights (optional)
model.load_weights('pretrained_weights.pth')

# Load an image
image = load_image('input.jpg')

# Perform object detection and segmentation
results = model.detect_and_segment(image)

# Save the results
save_results(results, 'output.jpg')

Contributing

Contributions to this project are welcome! If you would like to contribute, please follow these guidelines:

1. Fork the project.
2. Create a new branch for your feature or bug fix.
3. Make your changes and submit a pull request.