Instructions to use ProbeX/Model-J__ResNet__model_idx_0821 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_0821 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_0821") 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_0821") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0821") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2d274738aec8b659042aedbf93a152e10a923db2d688c520b66a88c42664a28
- Size of remote file:
- 5.37 kB
- SHA256:
- 1dce93b84f2f9f1ec91971ff7ee6e94b1360b42fb37622661c09dd502b339c49
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