Instructions to use ProbeX/Model-J__ResNet__model_idx_0889 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0889 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0889") 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__ResNet__model_idx_0889") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0889") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a02d6e0b8bcfa492ed4eabfea6a942f32d59d4914bc5c4e5a30b8451c10bfdc
- Size of remote file:
- 5.37 kB
- SHA256:
- 48743513619dd98f92e37a4ab6ebf7c6a7cab3daa2a004341ff2748fff8d1db5
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