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Real-time semantic segmentation for efficient scene understanding.

BiSeNet is a real-time semantic segmentation network designed for efficient scene understanding. It addresses the challenge of balancing accuracy and speed in deep learning models for tasks like autonomous driving and video surveillance. The architecture consists of two branches: a Spatial Path for preserving spatial details and a Context Path with a fast downsampling strategy to obtain sufficient receptive field. These paths are fused to generate high-resolution segmentation maps. The implementation provided supports both BiSeNetV1 and BiSeNetV2, with pretrained weights available for Cityscapes, COCOStuff, and ADE20k datasets. It provides tools for training, evaluation, and deployment using TensorRT, ncnn, OpenVINO, and Triton Inference Server. The model's performance is benchmarked on various datasets, offering competitive mIOU and FPS metrics.
BiSeNet is a real-time semantic segmentation network designed for efficient scene understanding.
Explore all tools that specialize in semantic segmentation. This domain focus ensures BiSeNet delivers optimized results for this specific requirement.
Achieves high FPS (frames per second) on various hardware platforms, enabling real-time applications.
Supports single-scale and multi-scale evaluation with flip augmentations for improved accuracy.
Optimized for deployment on NVIDIA GPUs using TensorRT and other inference engines like ncnn and OpenVINO.
Provides pretrained models on popular datasets like Cityscapes, COCOStuff, and ADE20k.
Highlights the variance in model performance across multiple training runs, providing insights into model stability.
Integration with Triton Inference Server for scalable and efficient deployment.
1. Clone the BiSeNet repository from GitHub.
2. Install the required dependencies: PyTorch, CUDA, cuDNN.
3. Download pretrained weights for your desired dataset (Cityscapes, COCOStuff, ADE20k).
4. Prepare the dataset by downloading and organizing the images and annotations according to the instructions.
5. Configure the training or inference settings in the config files.
6. Run the demo script to test the model on a single image or video.
7. Train the model on a custom dataset by modifying the configuration file and annotation paths.
All Set
Ready to go
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