Instructions to use ProbeX/Model-J__ResNet__model_idx_0721 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_0721 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_0721") 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_0721") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0721") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d990efd13d2d44d4e8ffea3bd97c446767184aec031b06932cacfa49d920803d
- Size of remote file:
- 171 MB
- SHA256:
- 4c48e749648616dcd20ef8c4444694ea1618b6e6d0c14eb2d578e7d49ab52cef
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