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:
- 920e327b96affbacbd4557adb3ee8dfba9f21740c379e6fd26fd62339d524cee
- Size of remote file:
- 3.96 kB
- SHA256:
- e9e21eacfe7e2bf3a028c943b8b4bd444000af510717a22ec0768b26a61ab24a
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