resnet50-finetuned-scenario16
RichardsonI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
krishidrishti-plant-disease
ripro805This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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查看详情 ↗gender-classification-2
rizvandwikiCreate your own image classifier for **anything** by running the demo on Google Colab. Report any issues with the demo at the github repo.
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查看详情 ↗plant-disease-dinov2-small
rodynaemadIdentifies **38 crop–condition classes** (26 diseases and 12 healthy classes across 14 crops) from a photo of a single leaf. Built as the open disease-scanning model for BloomTech, my AI + IoT smart-agriculture graduation project. PlantVillage contains several photos of the **same physical leaf**. If photos are split randomly, near-duplicates of a test leaf end up in training, which inflates test accuracy. The dataset authors' `leaf-map.json` links 40,490 photos to 7,524 physical leaves. I split the data so that **every photo of a given leaf lands on only one side** (stratified, leaf-grouped 80/20 split), and asserted that no leaf crosses the split. For this model the inflation from a naive split is small (99.1% vs 98.9%), but the leaf-level number is the one to trust. The remaining 13,815 photos have no leaf mapping and are split photo by photo. `onnx/model_with_features.onnx` (88 MB, fp32) and `onnx/model_with_features_quantized.onnx` (24 MB, int8) take `pixel_values` (1×3×224×224, ImageNet-normalized) and return `logits` and the 768-d `features` used for the out-of-distribution check. The fp32 export matches PyTorch exactly. The int8 copy, which powers the Space, scores **98.7% accuracy (98.1% macro F1)** on the full leaf-level test set. Example with onnxruntime-web in the Space's `index.html`.
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查看详情 ↗SDXL-Deepfake-Detector
SadraCoding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 ↗poolformer_s12
sailPoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository. PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.
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查看详情 ↗ifoodie-classifier-v8
sainteye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 ↗ifoodie-detail-rating-v15.2
sainteye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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sainteye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 ↗ifoodie-rating-reset-v13
sainteye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 ↗is-this-furry
SaintGermain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 ↗sarox-plangton
sarang-sketch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 ↗calorie-lens
shalev396A photo of a meal goes in; out come **the ingredients on the plate, how many grams of each, and the nutrition of every ingredient and of the whole plate** (kcal, protein, fat, carbs, fiber, sugars, saturated fat, sodium). It is two models fused into one network, trained on the Nutrition5k dishes, whose every ingredient was weighed on a scale, on plates weighed at a Finnish lunch line (FlavoriaFoodWeight1700) and on USDA FoodData Central. `config.json` records `pretrained: false`; `model.load()` rebuilds every network from `config.json` and loads `model.safetensors`, so inference never downloads a base model. The weights load with `transformers` versions before and after 5.18, which renamed DINOv2's attention layers. **Space API (free):** the Space exposes `/predict` (photo + optional total grams in, `[result, seconds, device]` out).
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查看详情 ↗UL_base_classification
sharmajai901This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3125 - Accuracy: 0.8921 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7
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查看详情 ↗UL_bedroom_classification
sharmajai901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 ↗UL_exterior_classification
sharmajai901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 ↗UL_interior_classification
sharmajai901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 ↗crop_leaf_diseases_vit
shehryars715> **Copy of `wambugu71/crop_leaf_diseases_vit`** at revision `7d5b32bcd6f83a2f57e7e0346358fad276296877`, mirrored unchanged for the CropSaathi app. All credit to the original author, Wambugu Kinyua; the original license applies. This model is a Vision Transformer (ViT) designed to identify plant diseases in crops as part of a smart agricultural farming system. It has been trained on a diverse dataset of plant images, including different disease categories affecting crops such as corn, potato, rice, and wheat. The model aims to provide farmers and agronomists with real-time disease detection for better crop management. This Vision Transformer model has been fine-tuned to classify various plant diseases commonly found in agricultural settings. The model can classify diseases in crops such as corn, potato, rice, and wheat, identifying diseases like rust, blight, leaf spots, and others. The goal is to enable precision farming by helping farmers detect diseases early and take appropriate actions.
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查看详情 ↗Brain-Tumor-Detection
ShimaGh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 ↗serverless-roomsort
ShuaHousetable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 ↗pixai-tagger-v1.0
sibwvj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 ↗Pokemon-classifier-gen9-1025
skshmjn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 ↗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 ↗densenet121.imagenet
smp-hub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。