Instructions to use ProbeX/Model-J__ResNet__model_idx_0709 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_0709 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_0709") 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_0709") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0709", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f6e4c55627f50b3c66148ee04acb99e9015aad7bbfa0abedafe9830c551ffda
- Size of remote file:
- 171 MB
- SHA256:
- 0daccceda5b3215ad8c7e2457613cb8fd068b86829852c31d923420d386d7fcf
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