Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use 02shanky/test_model_graphics_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 02shanky/test_model_graphics_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="02shanky/test_model_graphics_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("02shanky/test_model_graphics_classification") model = AutoModelForImageClassification.from_pretrained("02shanky/test_model_graphics_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f041681f72d312aedcbe90be7561f9327f9f4f75513a3f2b12a3ed5cffbff1b5
- Size of remote file:
- 343 MB
- SHA256:
- ea83d074b22772561160a490e35d1b1611c84b2dc0efc4d07feb302d0b349e04
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