DRL-Active-Object-Detection

Deep Reinforcement Learning for Active Object Detection: A novel approach that combines deep reinforcement learning with active learning strategies to improve object detection performance while minimizing annotation costs.

Key Features

• Utilizes a deep learning-based object detection architecture, such as Faster R-CNN.
• Incorporates a deep reinforcement learning agent to actively select informative and diverse samples for annotation.
• Employs active learning strategies, such as uncertainty estimation and curriculum learning, to improve training efficiency and detection performance.
• Provides a modular and flexible implementation for easy experimentation with different components and techniques.

Installation

1. Clone the repository

   git clone https://github.com/username/DRL-ActiveObjectDetection.git
   cd DRL-ActiveObjectDetection

2. Install the required dependencies

   pip install -r requirements.txt

Usage

1. Download and preprocess the COCO dataset (or any other desired dataset) and place it in the datasets/ directory.
2. Configure the object detection architecture, training parameters, and reinforcement learning agent settings in the config/ directory.
3. Train and evaluate the proposed model:

   python main.py

Results

Contributing

Contributions to this project are welcome! Please open an issue or submit a pull request if you have any ideas, suggestions, or improvements.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for more information.