Image Segmentation Toolkit
Open-source toolkit for semantic and instance segmentation
About
A comprehensive toolkit supporting multiple architectures including DeepLabV3+, Mask R-CNN, and U-Net. Features include model zoo, training pipeline, and inference optimization. ## Key Features - **Multiple Architectures**: Support for DeepLabV3+, Mask R-CNN, U-Net, and more - **Pre-trained Models**: Zoo of models trained on COCO, Pascal VOC, and Cityscapes - **Easy-to-use API**: Simple Python interface for quick integration - **Training Pipeline**: Complete training workflow with distributed training support - **Inference Optimization**: ONNX export and TensorRT acceleration - **Active Community**: Regular updates and responsive maintainers ## Performance | Architecture | Backbone | mAP (COCO) | FPS (RTX 3090) | |--------------|----------|------------|----------------| | Mask R-CNN | ResNet-50 | 37.2 | 45 | | DeepLabV3+ | ResNet-101 | 43.1 | 38 | | U-Net | ResNet-34 | 32.8 | 52 | ## Installation ```bash pip install segmentation-toolkit ``` ## Quick Start ```python from segmentation_toolkit import MaskRCNN model = MaskRCNN.from_pretrained("maskrcnn-resnet50-coco") result = model.predict("image.jpg") result.save("output.jpg") ```