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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e1b595b72a07223820a0747d0e7b63939a475b8346d547509dffaf518101725
- Size of remote file:
- 110 MB
- SHA256:
- 823b7b2e788156905ea8461a8ec0659ef03fafd840aef929b38c4214ea1aa8e1
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