Instructions to use ahmedesmail16/Project_Class_Model_beit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Project_Class_Model_beit-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Project_Class_Model_beit-base-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_beit-base-patch16") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_Model_beit-base-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 143bff1a7f86dfd091efc175a44cbeac6470dac55b3477980b4208e78f9e3229
- Size of remote file:
- 343 MB
- SHA256:
- 5acc103f84ddc44f88511ff48c1974c1f4b1e1b81e7c648bb97d61d2a4f4634f
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