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")# pip install -U transformers accelerate # 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
Training in progress, step 90
Browse files
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