Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Hemg/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/working") 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("Hemg/working") model = AutoModelForImageClassification.from_pretrained("Hemg/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea4e8c1bae94443853b2babc5f8ea8336d15567fc74d9d6da5d01b8efe799595
- Size of remote file:
- 4.86 kB
- SHA256:
- 411a6e71950220c0d17cd849b220974175efd0891ddac787332af1d4e7711e2f
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