Instructions to use ahmedesmail16/Train-Test-Augmentation-V4-beit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Train-Test-Augmentation-V4-beit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Train-Test-Augmentation-V4-beit-base") 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/Train-Test-Augmentation-V4-beit-base") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Train-Test-Augmentation-V4-beit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b7421cad94ea35005916aeb564444ebcfe6e38fbda17658b6968b401e62395d
- Size of remote file:
- 4.98 kB
- SHA256:
- 3708b1972111dd62bfe5b9391cee57032a9cecaaa308cfefd794afa710962fa5
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