BiRefNet-GGUF
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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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗RMBG-2.0
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查看详情 ↗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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查看详情 ↗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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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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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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查看详情 ↗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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CIDAS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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查看详情 ↗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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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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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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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detr-resnet-101-panoptic
facebook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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查看详情 ↗detr-resnet-50-dc5-panoptic
facebook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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查看详情 ↗detr-resnet-50-panoptic
facebookDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
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查看详情 ↗Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。