Instructions to use ProbeX/Model-J__ResNet__model_idx_0734 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_0734 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_0734") 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_0734") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0734") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0d59ead3407979c5b79e53287cb2e0f50886efc907045851f70cb951afbd324
- Size of remote file:
- 5.37 kB
- SHA256:
- 13109ae406b943d63798f0824c424527dd2ae8c5fb70ed7f0bdb2100368f6ee5
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