Instructions to use miladfa7/vit-product-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miladfa7/vit-product-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="miladfa7/vit-product-model") 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("miladfa7/vit-product-model") model = AutoModelForImageClassification.from_pretrained("miladfa7/vit-product-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9466a5b5ae0ded20758a5633297d3dd3340ed548af39154b7ad697a51650dbe9
- Size of remote file:
- 343 MB
- SHA256:
- a4dab6d0c1d8a3d83224ba85e179c7dbe3dfb4311f61d29c301b023532a18caa
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