Instructions to use ProbeX/Model-J__ResNet__model_idx_0120 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_0120 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_0120") 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_0120") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0120", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0850ed7440ab9dbfbeb595e2768777b878e7bf9520f2a9ae1ceba8ffb3425690
- Size of remote file:
- 171 MB
- SHA256:
- c84ac4b3c57ab147598bd2893ffdda0a95df63307407576f354df2820e9c3681
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