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:
- b9e39b47f5240c1bce92e318965faa5dddf7aea9a30aa6ec933cef7168e62dc2
- Size of remote file:
- 3.44 kB
- SHA256:
- 07ade1add86391377f19e0cedaf913e854c9cafadc84852a11d24cb276fd4a21
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