Some Chives SF

CLIPSeg is an image segmentation model based on text and image prompts, supporting zero-shot and one-shot image segmentation tasks.

BRIA RMBG v1.4 is an advanced background removal model designed for efficiently separating foreground and background in various types of images, suitable for non-commercial use.

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.

SegFormer model fine-tuned on ATR dataset for clothing and human segmentation

SAM is a vision model capable of generating high-quality object masks from input prompts (such as points or boxes), supporting zero-shot segmentation tasks

BiRefNet is a deep learning model for high-resolution binary image segmentation, which achieves accurate image segmentation through a bilateral reference network.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20K dataset, suitable for image segmentation tasks.

SAM is a visual model capable of generating high-quality object masks from input points or bounding boxes, with zero-shot transfer capability.

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

SAM is a vision model capable of generating high-quality object masks based on input prompts, supporting zero-shot transfer to new tasks