Instructions to use ProbeX/Model-J__ResNet__model_idx_0028 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_0028 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_0028") 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_0028") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0028", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3587b36e481ac877eb78cac162cd50b51db5284cfbbfb6aad6eb339dade002e0
- Size of remote file:
- 5.37 kB
- SHA256:
- 419bd2e895d4d3524931681ccfd6ee89e739478c9a78d304eb57e789aa32fd6a
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