Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use Soulaimen/swin-tiny-patch4-window7-224-bottomCleanedData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soulaimen/swin-tiny-patch4-window7-224-bottomCleanedData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Soulaimen/swin-tiny-patch4-window7-224-bottomCleanedData") 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("Soulaimen/swin-tiny-patch4-window7-224-bottomCleanedData") model = AutoModelForImageClassification.from_pretrained("Soulaimen/swin-tiny-patch4-window7-224-bottomCleanedData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8f395b6a43369aab3f2079661b0e6b46c0700489be8c21c5fb1a9405523743a
- Size of remote file:
- 110 MB
- SHA256:
- b532510aa42a8ca499728ecf9e0865fec6500bb5bcf9bc8b0b8e7f9cefe5f38e
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