Instructions to use pytholic/vit_classification_huggingface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pytholic/vit_classification_huggingface with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pytholic/vit_classification_huggingface") 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("pytholic/vit_classification_huggingface") model = AutoModelForImageClassification.from_pretrained("pytholic/vit_classification_huggingface", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: vit_classification_huggingface | |
| results: | |
| - task: | |
| name: Animal-10 Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.980894148349762 | |
| # vit_classification_huggingface | |
| Animal-10 dataset classification using Vision Transformer with Hugging Face. | |
| ## Example Images | |
| #### cane | |
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| #### cavallo | |
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| #### elefante | |
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| #### farfalla | |
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| #### gallina | |
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| #### gatto | |
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| #### mucca | |
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| #### pecora | |
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| #### ragno | |
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| #### scoiattolo | |
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