Instructions to use ahmedesmail16/Project_Class_Model_vit-large-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Project_Class_Model_vit-large-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Project_Class_Model_vit-large-patch16") 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_Model_vit-large-patch16") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_Model_vit-large-patch16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1062045a037fb4077d35f72655206cd63a9c041d9fa2fe41ca572c9a337a66a1
- Size of remote file:
- 1.21 GB
- SHA256:
- dd17405c529fa5d79027328cff46f1b4577aa0c0014c6eac14b186afeb88072a
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