Instructions to use ProbeX/Model-J__ResNet__model_idx_0296 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_0296 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_0296") 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_0296") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0296") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5232d248c362a31d7f9ef40dabdabea50c9af3f6a839692617c4047dc3072f90
- Size of remote file:
- 5.37 kB
- SHA256:
- d0b7d1a9e135b68b39df578c217307f63efb971ec8685d08690c2df398201f39
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