Instructions to use ProbeX/Model-J__ResNet__model_idx_0310 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_0310 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_0310") 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_0310") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0310", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b9aeb35f881390a19356d6640516d0887f1040f7418ba6f69bb3d21f45e1967
- Size of remote file:
- 5.37 kB
- SHA256:
- ca1ec04fb1f5977e6c4fe94788acbacd8dbaf4177a47d5fd1ac78b8e1eefe452
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