Instructions to use ProbeX/Model-J__ResNet__model_idx_0205 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_0205 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_0205") 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_0205") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0205") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6579612d647bfbe6e64698e3ec9ff6448570bad2742cc7faed8f2b07ce03b113
- Size of remote file:
- 5.37 kB
- SHA256:
- c5e1c8b4b6a7cc5a44ed6d919fa5b4bab0ec4eac86612f8182a9eb1e49849e0d
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