Instructions to use SrimathiE21ALR044/Cattle_Skin_Disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SrimathiE21ALR044/Cattle_Skin_Disease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SrimathiE21ALR044/Cattle_Skin_Disease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("SrimathiE21ALR044/Cattle_Skin_Disease") model = AutoModelForImageClassification.from_pretrained("SrimathiE21ALR044/Cattle_Skin_Disease") - Notebooks
- Google Colab
- Kaggle
Cattle_Skin_Disease
This project utilizes a Vision Transformer (ViT) model to classify cattle skin diseases, leveraging a curated dataset that was augmented to enhance diversity and robustness. The dataset, enriched through data augmentation techniques, provides a varied and comprehensive collection of high-resolution images. By employing the ViT model, trained on this augmented dataset, the project aims to enhance veterinarians' diagnostic accuracy. This innovative approach not only optimizes disease recognition but also contributes to the advancement of machine learning applications in veterinary medicine.
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Example Images
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Evaluation results
- Accuracyself-reported1.000
