Instructions to use ProbeX/Model-J__SupViT__model_idx_0125 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0125 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0125") 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("ProbeX/Model-J__SupViT__model_idx_0125") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0125") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09404fe88c04d200a0d9c9055077e068452d054c2da3c3ec7dde6de83fce0f73
- Size of remote file:
- 5.37 kB
- SHA256:
- 6a0889f09e9f04d29be66b81f550a30e9cbfc2cf7696c20253e5cf38fc753ffc
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