Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-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("djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") model = AutoModelForImageClassification.from_pretrained("djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ac5ce6ea8c7b007b7895d6c69fd67a95c0c167576fb743cb83676730d7e347d
- Size of remote file:
- 4.78 kB
- SHA256:
- d7c67ba80d55d5c05f97d2324178fe90dccb4013f8910785cce2d6bab4b8c1e9
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