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.
- 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.
- Clone the repository
git clone https://github.com/username/DRL-ActiveObjectDetection.git
cd DRL-ActiveObjectDetection- Install the required dependencies
pip install -r requirements.txt
- Download and preprocess the COCO dataset (or any other desired dataset) and place it in the
datasets/directory. - Configure the object detection architecture, training parameters, and reinforcement learning agent settings in the
config/directory. - Train and evaluate the proposed model:
python main.py
Contributions to this project are welcome! Please open an issue or submit a pull request if you have any ideas, suggestions, or improvements.
This project is licensed under the Apache 2.0 License. See the LICENSE file for more information.