Instructions to use Enayat/ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enayat/ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enayat/ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Enayat/ViT") model = AutoModelForImageClassification.from_pretrained("Enayat/ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Enayat/ViT: direct link, hf CLI and curl.
- Browser
- Download file 734 Bytes
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https://huggingface.co/Enayat/ViT/resolve/main/README.md
- Command line
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hf download hf://Enayat/ViT/README.md
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curl -L -o README.md https://huggingface.co/Enayat/ViT/resolve/main/README.md
734 Bytes
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: ViT | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9299362897872925 | |
| # ViT | |
| Created a Simple Image Classifier using ViT Transformers, custom trained on blur-dataset with an acc of about 93%. | |
| The Dataset link : https://www.kaggle.com/datasets/kwentar/blur-dataset | |
| ## Architecture | |
|  | |
| ## Example Images | |
| #### defocused_blurred | |
|  | |
| #### motion_blurred | |
|  | |
| #### sharp | |
|  |