Instructions to use ahmedesmail16/Project_Class_Aug_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_Aug_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_Aug_Model_vit-large-patch16") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16/resolve/main/training_args.bin
- Command line
-
hf download hf://ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- b5d31087934a16abdcdd684e07c4e873d833fb49e743f39f98e9adfabe50df6e
- Size of remote file:
- 4.98 kB
- SHA256:
- 61adedc0c87cdc94aa0f618331461131740931df7410d909e842ee27d66eb4ba
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