Instructions to use ProbeX/Model-J__ResNet__model_idx_0191 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_0191 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_0191") 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_0191") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0191") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b91ad620132b45ab8cdbb225c6d4f6b2cc23154c375300c54e2153d807d34c6c
- Size of remote file:
- 171 MB
- SHA256:
- 9d1402c7a6424fe9612273c29a2e2c190d9b4b5a1de934f7fb0655bb7bbc9c14
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