Instructions to use ProbeX/Model-J__ResNet__model_idx_0979 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_0979 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_0979") 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_0979") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0979") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a119bd623854ff5a3d93a4506caf9f342448f288f920842d7e84939053199d3f
- Size of remote file:
- 5.37 kB
- SHA256:
- adee1a86290853bd4be78e1a7e936d31836f377f79e21b71ed53647b1bb1fa7a
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