Image Classification
Transformers
TensorBoard
Safetensors
vit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use punchnami/ViT-Base-Pothole-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use punchnami/ViT-Base-Pothole-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="punchnami/ViT-Base-Pothole-Classification") 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("punchnami/ViT-Base-Pothole-Classification") model = AutoModelForImageClassification.from_pretrained("punchnami/ViT-Base-Pothole-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54542d741b22560f9a4d3b9b7a43aed268f644a9380b755e815362bd6ab97604
- Size of remote file:
- 343 MB
- SHA256:
- ccb2e422b34b1bf09d4e5695b10167aa257ea5bb2a476279bc96c03a050871d2
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