Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Dhika/raildefectfft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefectfft2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefectfft2") 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("Dhika/raildefectfft2") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefectfft2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2372cc2a7a7e6b51ca2536bcf62cb763bd788fdc1875369c0303c2967c557e3f
- Size of remote file:
- 3.9 kB
- SHA256:
- d774aec3c53640c9a6f3a1cebb284ed2c3a1e33a24918d0652dc584ae95be96b
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