Instructions to use vlevi/Main_fashion-swin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vlevi/Main_fashion-swin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vlevi/Main_fashion-swin") 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("vlevi/Main_fashion-swin") model = AutoModelForImageClassification.from_pretrained("vlevi/Main_fashion-swin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d31559692816ef4822779bbb5c61cecad371beeacbb56316d6b0d848be5997f8
- Size of remote file:
- 110 MB
- SHA256:
- d9a7333a680ea15c29b2a8e262892fd747b5f17e7e1b3e977860d714430c06b1
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