Instructions to use ahmedesmail16/Train-Augmentation-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Train-Augmentation-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Train-Augmentation-vit-base") 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("ahmedesmail16/Train-Augmentation-vit-base") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Train-Augmentation-vit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4aadd7958ab73c146de81a00ee9cbe9616761d2039174b13c461317971b6cf1d
- Size of remote file:
- 4.98 kB
- SHA256:
- c6371a42bb521d83d27815b8cad0ae10f44286cc4f9f0781e695fb3165772897
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