Instructions to use ProbeX/Model-J__ResNet__model_idx_0831 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_0831 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_0831") 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_0831") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0831") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91cd2d14df3f4b0a2f1e22b8a5aace33c441b4ae6b58039fc977aa1c92fb0ae5
- Size of remote file:
- 5.37 kB
- SHA256:
- d432c1c4802d1b0c6f63aefb9503b0389d059a5697b5c3649a8a037b323bf113
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