BiRefNet-GGUF
Acly基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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manga-page-element-segmentation
anonimkaq4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗yolo11l-food-segmentation
arunapb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗segformer-v1-breastcancer
as-cle-bert基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RMBG-1.4
briaaiRMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of categories and image types. This model has been trained on a carefully selected dataset, which includes: general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. The accuracy, efficiency, and versatility currently rival leading source-available models. It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount. Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use. To purchase a commercial license, simply click Here.
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View details ↗RMBG-2.0
briaai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗U-2-Netp
BritishWerewolfU-2-Netp is a lightweight version of the U2Net model designed for efficient and effective image segmentation tasks, especially for generating masks. It retains the core architectural design of U2Net while being optimized for faster inference times and reduced memory usage. The U-2-Netp model is based on a simplified version of the original U2Net architecture, designed to be more lightweight while still achieving high performance in segmentation tasks. The model consists of several stages with down-sampling and up-sampling paths, using Residual U-blocks (RSU) for enhanced feature representation. To use the model for inference, you can follow the example provided above. The `AutoProcessor` and `AutoModel` classes from the `transformers` library make it easy to load the model and processor.
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View details ↗TurtleDetector
BVRA基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vesselFM
bwittmann基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗RMBG-2.0
camenduru基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗maggie-image-him50k-cvpr24
chuonghm基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗clipseg-rd64
CIDASCLIPSeg model with reduce dimension 64. It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository.
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View details ↗clipseg-rd64-refined
CIDASCLIPSeg model with reduce dimension 64, refined (using a more complex convolution). It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository. Refer to the documentation.
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View details ↗ChoroidSegmentationModule
ClementP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗rm
cocktailpeanut基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗comma10k-segnet
commaai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗layerd-birefnet
cyberagentThis repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/2509.25134), a layer decomposition framework for graphic design images. The model in this repository is **intended to be used as a part of the original LayerD github repository**. Please visit https://github.com/CyberAgentAILab/LayerD for more information. The model architecture code is based on the BiRefNet repository. We thank the authors for releasing their high-quality matting model. This repository is intended for use with LayerD, so we recommend following the instructions in the LayerD repository.
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View details ↗sam2.1_hiera_tiny
danelcsb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗yolo11-seg
EdgeFirst基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗yolov8-seg
EdgeFirst基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lucida
egeorcunLucida is a BiRefNet-based background-removal / image-matting model fine-tuned to excel where most open models fail: **camouflaged objects, transparent materials (glass), text & logos, VFX glows, and illustrations** — while staying competitive everywhere else. On our 203-image, 9-category benchmark (MAE, lower is better) Lucida leads every model we tested — including a commercial reference — in **camouflage (0.0270)** and **illustration (0.0092)**, beats the commercial reference in **text/logo preservation (0.0091 vs 0.0123)** and in **print-design/sticker art (0.0235 — 2x better than every model measured)**, and sets our best-ever **transparency-in-mixed-objects** handling and **overall (0.0257)** score — ahead of every model we measured, specialist or commercial, on the 203-image average. Full benchmark, gallery and training recipe: **https://github.com/egeorcun/lucida** — or try the live demo. > **Changelog note (2026-07-24):** an experimental v13 build was published for a day and then > reverted — community testing showed it regressed on real-world layered artwork > (poster/collage-style illustrations) that our synthetic design test set does not cover. > The current weights are the proven v7. The v13 improvements (reduced background haze on > real photos, a transparency milestone) will return in v14 together with the fix.
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