Image Classification
Transformers
PyTorch
TensorBoard
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use Gokulapriyan/swinv2-tiny-patch4-window8-256-finetuned-og_dataset_5e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gokulapriyan/swinv2-tiny-patch4-window8-256-finetuned-og_dataset_5e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Gokulapriyan/swinv2-tiny-patch4-window8-256-finetuned-og_dataset_5e") 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("Gokulapriyan/swinv2-tiny-patch4-window8-256-finetuned-og_dataset_5e") model = AutoModelForImageClassification.from_pretrained("Gokulapriyan/swinv2-tiny-patch4-window8-256-finetuned-og_dataset_5e") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4aef086ebf3689209444e44ec70ba2d5e8525e84d6cb7d1aac5197fa4c428c74
- Size of remote file:
- 3.58 kB
- SHA256:
- 7fe2a46c9c6014c2190e37d1cefe860341611456ea7a446907be5e0b88324d61
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