# High-Precision Semantic Segmentation

PyTorch-based DeepLabV3+ image segmentation model supporting multiple encoder architectures

A pre-trained coastline segmentation model based on PyTorch's FPN architecture, suitable for coastline detection tasks in remote sensing images

A fashion image segmentation model based on the SegFormer architecture, specifically designed for fine segmentation of clothing and accessories

A PyTorch-based FPN model specifically designed for image segmentation tasks on the Oxford Pet dataset, providing efficient semantic segmentation capabilities.

This model is an image segmentation model fine-tuned on drone image datasets based on nvidia/segformer-b3-finetuned-ade-512-512, suitable for scene parsing tasks.

A semantic segmentation model fine-tuned on satellite image datasets based on NVIDIA SegFormer-B0 architecture, specifically designed for earthquake-related scenario analysis

A garment segmentation model based on the SegFormer architecture, fine-tuned on the cloth_parsing_mix dataset for precise identification and segmentation of different clothing parts.

Semantic segmentation model fine-tuned from nvidia/mit-b5 for face parsing tasks

A vision model for semantic segmentation of sidewalk scenes, capable of identifying and segmenting different elements in images.

A visual model for semantic segmentation of sidewalk scenes, capable of identifying and segmenting different objects and areas on sidewalks.

This is a vision model for sidewalk semantic segmentation, capable of identifying and segmenting sidewalk areas in images.

This model is a question-answering system fine-tuned from deepset/roberta-base-squad2, specifically designed to extract answers from given texts.