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
Training in progress, epoch 1
Browse files
config.json
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"_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
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pytorch_model.bin
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runs/Sep06_02-47-48_8a3aa9d02c1c/1662432533.2764068/events.out.tfevents.1662432533.8a3aa9d02c1c.69.1
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training_args.bin
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