ai-image-detector-deploy
haywoodsloan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 ↗DISCOVER & COMPARE
探索 AI 模型 1529
ai-image-detector-dev-deploy
haywoodsloan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 ↗rev-ct
hcltech-robotics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 ↗resnet10
helper2424It's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. This model was converted using an automated JAX to PyTorch conversion pipeline, ensuring: - Weight preservation - Architecture matching - Numerical stability
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查看详情 ↗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 ↗howest-hf-ai
Hiradinho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 ↗emotion_classification
hmrizal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 ↗birdclass-na
houlette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 ↗Names a specimen crop at **subfamily, tribe, genus and species** for **400 bark and ambrosia beetle species** (115 genera, 26 tribes, 2 subfamilies), each level with a calibrated probability and a "known / unsure" decision, and reports the deepest level it is confident about: a species, "Genus sp. (species undetermined)", "Tribe (genus undetermined)", "Subfamily (tribe undetermined)" or "unrecognised". It is tuned to **avoid wrong names**: by default it stops at a coarser level rather than guess. The full species list is at the end of this card and in `species.csv`. Files: `model.safetensors` (weights), `deploy.safetensors` (kNN reference bank, validation score distributions), `config.json` (backbone, taxonomy, temperatures, thresholds, test report), `species.csv` (the 400 species), `calibration.md` (how the unknown check and thresholds were chosen). Classification of the benchmark's own species (top-1, no abstention): `iid_test` 650 scored specimens: subfamily 1.000, tribe 0.983, genus 0.922, species 0.838; all 5,017 `iid_test` specimens: genus 0.973, species 0.918; `inat_test` field photos: species 0.784.
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查看详情 ↗Imager-1
Imagers**Imager-1** is an AI model designed to detect AI-generated images with high precision; it can detect DALL-E 2. We will soon launch **imager-1-small** and **imager-2**, which will be able to detect DALL-E 3 and other text-to-image models. This repository contains the **base model** of Imager-1, released for **developers and researchers** who want direct access to the architecture, weights, and training pipeline. Imager always publishes a **separately verified and production-ready version** of each model, tested against real-world generators before release. This base model is provided so the community can build on top of it, fine-tune it, or integrate it into their own systems.
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查看详情 ↗imager-1-pro
Imagers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 ↗skintelligent-acne
imfarzanansariThis model card provides documentation for the Acne Severity Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess acne severity levels, ranging from clear skin to very severe acne. This Acne Severity Detection model checkpoint is licensed under the MIT License. Please review and adhere to the license when using or modifying the code.
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查看详情 ↗skintelligent-wrinkles
imfarzanansariThis model card provides documentation for the Wrinkles Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess wrinkle / non-wrinkle. The checkpoint includes the following files: - `config.json`: Model configuration settings. - `model.safetensors`: Serialized model parameters and architecture. - `preprocessor_config.json`: Configuration file for the preprocessor. - `training_args.bin`: Binary file storing training arguments.
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查看详情 ↗pokemons-image-classifier-gen1-gen9
imzynoxprince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 ↗document-gate-keeper-v0.5
InceptiveA lightweight binary classifier that tells whether a **document region crop** is legible enough to be sent to an OCR model. It labels each crop `good` or `bad`. It is meant to sit between a layout detector and a (vision-language) OCR model. Crops are produced by a detector such as `Inceptive/PP-DocLayoutV3-ONNX`, and this model flags the ones that are too blurry, pixelated or compression-damaged. The pipeline can then skip those regions instead of running expensive inference on them, since unreadable regions are a common source of hallucinated OCR output. The previous release, `Inceptive/document-gate-keeper-v0`, stays available unchanged. **v0.5 expects different preprocessing**, so do not reuse v0's code (see Note for v0 users).
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查看详情 ↗shieldgemma-2-4b-it-GGUF
infil00p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-frijol
inghggw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 ↗Trash-Net
introvoyz045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-oxford-iiit-pets
ISxOdinThis model is a fine-tuned version of google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1924 - Accuracy: 0.9445 This model is a fine-tuned version of a pre-trained Vision Transformer (`google/vit-base-patch16-224`) for image classification on the Oxford-IIIT Pet Dataset. It uses transfer learning to adapt a generic vision model to identify 37 different cat and dog breeds. The model head is adjusted to output the number of classes in the dataset, and it is trained end-to-end using standard classification loss. **Intended Uses:** - Educational demos on transfer learning and fine-tuning vision models. - Pet breed classification in structured datasets similar to Oxford Pets. - Comparative analysis with zero-shot models like CLIP.
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查看详情 ↗breastcancer-ultrasound-ViT
ITSheep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 ↗genderage2
ivensamdh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 ↗potato-disease-convnext
ixrbhii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 ↗silva-aesthetic
JannchieScores an illustration by **one specific person's** taste — not a universal quality model, so it won't match anyone else's preferences. Output is a single number in `[0, 1]`; higher means more to this person's liking. **Only the head ships here (~7 MB), not an image model.** It runs on top of the frozen `google/siglip2-so400m-patch14-384` backbone, which `silva[backbone]` installs and loads for you. Already have `google/siglip2-so400m-patch14-384` embeddings? Skip the backbone and score them directly:
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查看详情 ↗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。