Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use RohanK447/swin-tiny-patch4-window7-224-finetuned-vosap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RohanK447/swin-tiny-patch4-window7-224-finetuned-vosap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RohanK447/swin-tiny-patch4-window7-224-finetuned-vosap") 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("RohanK447/swin-tiny-patch4-window7-224-finetuned-vosap") model = AutoModelForImageClassification.from_pretrained("RohanK447/swin-tiny-patch4-window7-224-finetuned-vosap") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ae529d226262dc3fb66daba4c23a6d1da21674fb1b342e89a1092d728a3e021
- Size of remote file:
- 110 MB
- SHA256:
- c806b926fc090bef7611930f03b20a8b670b499561dd4e5b67147eaf0d8b6481
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