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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8bcdeb712b833b1cb27ef91d6c79617a5a473dec69bada2382d811ab133aa0b
- Size of remote file:
- 4.98 kB
- SHA256:
- eee21a3ed8629189fee9c5567bbd985627753a07960974e2ee4a2ce96646b3d3
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