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This 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).

Image classification
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Identifies **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`.

Image classification
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PoolFormer 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.

Image classification
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calorie-lens

shalev396

A 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).

Image classification
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This 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

Image classification
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> **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.

Image classification
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