# High-precision image segmentation

MODNet is a real-time trimap-free portrait matting model capable of efficient image segmentation and background removal.

An image segmentation model fine-tuned based on nvidia/mit-b3, performing excellently on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset

SegFormer image segmentation model based on NVIDIA MIT-B1 architecture, fine-tuned on specific datasets, excels in high-precision image segmentation tasks

An image segmentation model fine-tuned based on nvidia/mit-b3, trained on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset, excelling in high-precision image segmentation tasks.

A lightweight image segmentation model based on the SegFormer architecture, fine-tuned on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset, specifically designed for high-precision semantic segmentation tasks.

An image segmentation model fine-tuned based on nvidia/mit-b0, performing excellently on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset

PyTorch-based Unet image segmentation model supporting various encoder architectures and pre-trained weights

A PyTorch-based FPN image segmentation model supporting various encoder architectures, suitable for semantic segmentation tasks.

An image segmentation model fine-tuned based on the Segformer-b0 architecture, specifically designed for road crack detection tasks.

An image segmentation model based on the SegFormer architecture, pre-trained on the ADE20K dataset and further fine-tuned, excelling in 512x512 resolution image segmentation tasks

An image segmentation model fine-tuned based on nvidia/mit-b0, demonstrating outstanding performance on the evaluation set with a mean IoU of 0.9210 and mean accuracy of 0.9571.

An image segmentation model fine-tuned based on nvidia/mit-b0, trained on the NICOPOI-9/morphpad_hgo_512_4class dataset, excelling in high-precision image segmentation tasks.

A lightweight image segmentation model based on the SegFormer architecture, fine-tuned on specific datasets, excelling in high-precision pixel-level classification tasks

The latest background removal model developed by BRIA AI, capable of effectively separating foreground and background in various images, suitable for large-scale commercial content creation scenarios.

A vision segmentation model based on the SegFormer architecture, specifically designed for wall image segmentation tasks, achieving high accuracy and IoU metrics on the evaluation set.

An image segmentation model based on the SegFormer-B5 architecture, fine-tuned on larger-sized image datasets, excels at distinguishing between background and branch regions

This model is an image segmentation model fine-tuned on the JCAI2000/100By100BranchPNG dataset based on nvidia/mit-b5, primarily used for semantic segmentation tasks of branches and backgrounds.