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:
- 49919785c4ab4df7fec1de99b6eb6e5a9cc6943f7d5697e7ed7ed55f9953b1db
- Size of remote file:
- 3.64 kB
- SHA256:
- a71f58b0514499d12e0b5ebc00521b6670430d9a819e3a989744ffda1e837e35
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