Instructions to use akar49/VIT_fourclass_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akar49/VIT_fourclass_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="akar49/VIT_fourclass_classifier") 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("akar49/VIT_fourclass_classifier") model = AutoModelForImageClassification.from_pretrained("akar49/VIT_fourclass_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a636c4570fc9d988c7cef4121799fde74be141dea2860a1df53ca95e275dce7
- Size of remote file:
- 343 MB
- SHA256:
- f53ca4dd60a996b67e3d9967bc174fb0c4cc55c243a3de2a0836ba865df287a1
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