Instructions to use ProbeX/Model-J__ResNet__model_idx_0130 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_0130 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_0130") 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_0130") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0130") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cb3c5611222375beeecdde0f9763ec2875a33c947440cb83634a8f0f45736ce
- Size of remote file:
- 171 MB
- SHA256:
- 1bfb71b25f305b56df85c21b565c4f026ca168ccb938ee4f12dde7aa945acabc
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