Instructions to use ahmedesmail16/Project_Class_vitb16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Project_Class_vitb16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Project_Class_vitb16") 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_vitb16") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_vitb16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a8a7b62fa28f001c3a91992789cce703339b21550d36a4b4255541ce1b61486
- Size of remote file:
- 1.21 GB
- SHA256:
- 0e3a0afe868e4d1056d630314e6b549b71858501ce3b3c2358ed6e684da34515
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