Instructions to use ProbeX/Model-J__ResNet__model_idx_0413 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_0413 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_0413") 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_0413") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0413") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89d030264f2ff6164350d6ae1dfb46042b1321556d46c2ce4a43a0da2809dda4
- Size of remote file:
- 171 MB
- SHA256:
- 8e6e08651584d893ce82eba4afdf25e97408e6db50dbfe23df1fc5597a293515
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