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:
- 147251943a3434ed9931a39237dc137570a10ddc8e13549047bd101a4b5d73bd
- Size of remote file:
- 4.92 kB
- SHA256:
- b4ec1587566db042491d0a919d4ea4925552c30f158ffd24289a930706d7f975
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