Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease") model = AutoModelForImageClassification.from_pretrained("gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- 3b6be9f2431b4be7f5a38cf901ae6cca82e7f01a58834d2a39cda864ccacaca6
- Size of remote file:
- 110 MB
- SHA256:
- ccb33af8b4bfe6fe55abc9159159b85a16dcf0bef3f504f35981292922f20277
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