Instructions to use ahmedesmail16/Project_Class_vit-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Project_Class_vit-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Project_Class_vit-large") 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/Project_Class_vit-large") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_vit-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec202feb54aa63b4ca22a1b0b6671bb1b089a8efb58df2a12cda8b4a834c15df
- Size of remote file:
- 4.92 kB
- SHA256:
- 7861ec8e0d027464943c20fe6e843e7d489657c8c480829ca531d420182cf834
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