Instructions to use ProbeX/Model-J__ResNet__model_idx_0655 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_0655 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_0655") 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_0655") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0655", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 988b31dfcc2720995b8e4e31f320a9991f993b543244ebd39d8bf1640e51a816
- Size of remote file:
- 171 MB
- SHA256:
- b934c289afbd23756bec65acd8c17c03430a08e4cc49ab6170810bdfc0e8299d
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