This repository contains various semantic segmentation models implemented using TensorFlow 2.14. The models included are:
- DeepLab
- HRNet
- LSTM
- MACUNet
- ResUNet
- SegNet
- UNet
- UNet+++
- U-HRNet
- Multispectral Image Support: Both training and prediction scripts support multispectral image data.
- State-of-the-art Models: Includes popular and state-of-the-art semantic segmentation models tailored for remote sensing applications.
- TensorFlow 2.14
- Python 3.9
- Additional dependencies listed in
requirements.txt
-
Clone the repository:
git clone https://github.com/TadpoleL/tensorflow-based-semantic-segmentation-models-for-RS.git cd tensorflow-based-semantic-segmentation-models-for-RS -
Install required packages:
pip install -r requirements.txt
To train a model, run the training script with the appropriate configuration.
To perform prediction using a trained model, use the prediction script.
- DeepLab: A deep learning model for semantic image segmentation, offering several variants such as DeepLabV3 and DeepLabV3+.
- HRNet: High-Resolution Network, which maintains high-resolution representations through the whole process.
- LSTM: Long Short-Term Memory networks, adapted for segmentation tasks.
- MACUNet: A variation of UNet with additional attention mechanisms.
- ResUNet: UNet with residual connections.
- SegNet: A deep convolutional encoder-decoder architecture for image segmentation.
- UNet: A convolutional network architecture for fast and precise segmentation of images.
- UNet+++: An advanced version of UNet with nested and dense skip connections.
- U-HRNet: Combines the strengths of UNet and HRNet for improved segmentation performance.
Contributions are welcome! Please submit pull requests or open issues to contribute to this project.
This project is licensed under the MIT License - see the LICENSE file for details.