TADFormer: Efficient Multi-Task Learning with Dynamic Transformers

Table of Contents

• Overview
• Features
• Installation
• Usage
• Model Architecture
• Datasets
• Results
• Contributing
• License

Overview

TADFormer is an official implementation of the Task-Adaptive Dynamic Transformer, designed for efficient multi-task learning. This model optimizes performance across various tasks in computer vision, such as dense prediction and scene understanding. With TADFormer, researchers and developers can explore advanced techniques in parameter-efficient fine-tuning and dynamic filter networks.

You can find the latest releases here.

Features

• Dynamic Filter Networks: Adapts to different tasks for improved performance.
• Multi-Task Learning: Supports simultaneous training on multiple tasks.
• Parameter-Efficient Fine-Tuning: Reduces the number of parameters needed for fine-tuning.
• Support for Vision Transformers: Utilizes the latest advancements in transformer architecture.
• Visual Prompt Tuning: Enhances model adaptability to various visual tasks.

Installation

To get started with TADFormer, follow these steps:

1. Clone the repository:

   git clone https://github.com/punpunzaz10/TADFormer.git
   cd TADFormer

2. Install the required packages:

   pip install -r requirements.txt

3. Download the necessary datasets and models. You can find the latest releases here.

Usage

To train the model, use the following command:

python train.py --config config.yaml

Replace config.yaml with your desired configuration file. You can modify this file to adjust parameters for your specific tasks.

To evaluate the model, run:

python evaluate.py --model path/to/model.pth

Make sure to replace path/to/model.pth with the path to your trained model.

Model Architecture

TADFormer employs a unique architecture that integrates several key components:

• Task-Adaptive Layers: These layers dynamically adjust based on the task at hand, allowing for more efficient learning.
• Dynamic Filter Networks: These networks enable the model to apply different filters for different tasks, improving accuracy.
• Swin Transformer Backbone: The model utilizes the Swin Transformer architecture, known for its efficiency in handling visual data.

Architecture Diagram

Datasets

TADFormer is compatible with several datasets, including:

• Pascal Context: A dataset for semantic segmentation and scene understanding.
• COCO: Common Objects in Context, widely used for object detection tasks.
• Cityscapes: Focused on semantic segmentation in urban environments.

You can download these datasets from their respective sources. Ensure that the data is organized according to the model's requirements.

Results

TADFormer has shown impressive results across various benchmarks:

• Pascal Context: Achieved state-of-the-art performance in semantic segmentation.
• COCO: Demonstrated superior accuracy in object detection tasks.
• Cityscapes: Outperformed previous models in urban scene understanding.

For detailed metrics and comparisons, refer to the results section in the documentation.

Contributing

We welcome contributions from the community. If you want to contribute to TADFormer, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your branch and create a pull request.

Please ensure your code adheres to the project's style guidelines and includes relevant tests.

License

TADFormer is licensed under the MIT License. See the LICENSE file for more details.

For any questions or issues, feel free to open an issue in the repository or check the "Releases" section for updates.