regnet-y-320-seer-in1k
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探索 AI 模型 1529
regnet-y-640-seer-in1k
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查看详情 ↗superblock-vit-b-16
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查看详情 ↗joytag
fancyfeast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 ↗vit-base-mnist
farleyknight-org-username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 ↗Darwin-35B-ZTC-Vision
FINAL-BenchDarwin-35B-ZTC-Vision is a vision decision model. It does not generate text. Given one or more images, a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (`noul`), one of several options (`choice`), or a level on an ordered scale (`score`). It handles up to four images per request, for example "which of the two photos has the higher quality" or "is there a defect on this part". It is a 35B mixture-of-experts model (about 3B active parameters per token) built on Darwin-35B-A3B-Opus with the Darwin ZTC decision readout. It is the vision member of the ZTC series; the text member is Darwin-27B-ZTC-v9. All 20 required vision benchmarks complete (the optional Box tasks are not supported), zero failed decisions. Merged by the maintainer on 2026-10-19 (PR #76). At submission it ranked first (Borda 91.00) on the published vision results of 2026-10-17 (16 complete models) with this model added, recomputed with the leaderboard's own Borda method; that table is below. More models have entered since: on the published results of 2026-10-19 (32 complete models) the same recomputation places it fifth with 81.80. The live vision leaderboard is authoritative.
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查看详情 ↗dinov2-small-fire-associated-smoke-v2.5
fireviewerBinary **fire-associated smoke compatibility**, not smoke-source pointing or physical aerosol density. A probable-fire ROI with no confirmed visible flame can be routed here. Input: RGB crop with 45% context per side, minimum side 160, bicubic 448×448 resize and ImageNet normalization. DINOv2 ViT-S/14 CLS embedding (384) → LayerNorm → dropout 0.15 → linear logit. Output is temperature-scaled compatibility with a low/high decision and an abstention interval. Rejected smoke keypoints, masks and spatial-refiner runs are not included. Positive labels require retained provenance supporting fire-associated smoke or visible flame elsewhere in the source image. Crops containing a flame fragment are excluded, as are unsupported associations; ambiguous is an inference abstention state, not a third training label. Training membership is not gated by another model's predicted absence of flame. Linked v2.5-smoke-from-fire-probability: 50,183 train / 12,106 validation public ROIs, a rights-filtered subset of the retained full run corpus. Run `20261005-075018-dinov2_vits14`: 3 head-only epochs then 5 epochs with the last two backbone blocks unfrozen, batch 48, BF16, seed 42. Head LR 3e-4, backbone LR 5e-6, weight decay 0.05, 0.5-epoch warmup and cosine decay per phase. Best checkpoint is tail phase **E3 (overall E6)** by selection-fold AUPRC. Eight epochs completed. Official DINOv2 source is pinned at `7764ea0f912e53c92e82eb78a2a1631e92725fc8`.
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查看详情 ↗nsfw_image_detector
Freepik- Model Description - Try it Online! - Model Performance Comparison - Global Performance - Accuracy by AI Content - AI-Generated Content - Non-AI-Generated Content - Usage - Quick Start via pip - Quick Start with Pipeline - Avoid installation of pip dependency - Training - Speed and Memory Metrics This model is a vision transformer based on the **EVA architecture**, fine-tuned for **NSFW content classification**. It has been trained to detect **four categories** (neutral, low, medium, high) of visual content using **100,000 synthetically labeled images**. The model can be used as a **binary (true/false) classifier if desired, or you can obtain the full output probabilities.**. It **outperforms other excellent publicly available models** such as Falconsai/nsfw_image_detection or AdamCodd/vit-base-nsfw-detector in our internal benchmarks adding the enrichment of being able to select the NSFW level that suits your use case.
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查看详情 ↗repvgg_a0
frgfm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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fxmarty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 ↗plantclef2024
gerald29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 ↗bone-marrow-cells-yolo
ghostsas001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 ↗nsfw-classifier
giacomoarientiIn today's digital world, user-generated content is a double-edged sword. While it fosters creativity and engagement, it also opens the door to inappropriate or illegal content being shared. Our NSFW Image Classifier is specifically designed to identify and filter out explicit images, including **pornography, hentai, and sexually suggestive content**, ensuring your platform remains **safe, secure**, and **legally compliant**. - **Protect Your User Base**: Keep your community safe by preventing exposure to inappropriate content. - **Legal Compliance**: Avoid legal action by preventing illegal or explicit content from being posted. - **Seamless Integration**: Our model is easy to integrate into any platform that allows image uploads, including social media, forums, e-commerce sites, and more. With more than **2M downloads**, our NSFW Image Classifier has become the go-to solution for platforms looking to **maintain a clean and safe environment** for their users. Many developers and companies have already chosen our solution to protect their communities—will you be next?
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查看详情 ↗lepinet-bioclip2-vitl14
gmougeot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 ↗lepinet-dinov3-convnextl
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查看详情 ↗lepinet-effnetv2s
gmougeot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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bit-50
GoogleThe BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby. BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. *Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.*
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查看详情 ↗cxr-foundation
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查看详情 ↗derm-foundation
Google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 ↗efficientnet-b0
GoogleEfficientNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le, and first released in this repository. Disclaimer: The team releasing EfficientNet did not write a model card for this model so this model card has been written by the Hugging Face team. EfficientNet is a mobile friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.
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查看详情 ↗efficientnet-b1
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查看详情 ↗efficientnet-b2
Google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 ↗efficientnet-b3
Google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 ↗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。