Instructions to use ProbeX/Model-J__ResNet__model_idx_0784 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_0784 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_0784") 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_0784") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0784") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2c9dc979d49df5f9891851f1ce04557996b2545d0659102f1a200248cb6d5ea
- Size of remote file:
- 5.37 kB
- SHA256:
- d0772db8fb49616e22186207bc52e3ebb178b66d4c6d1e640336788b9f5e03dd
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