Instructions to use ProbeX/Model-J__ResNet__model_idx_0159 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_0159 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_0159") 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_0159") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0159") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8475c1530d6a1a26aea7a4b3bb8750c33dc0f39bb0e89b3ae559dcf8765a0f50
- Size of remote file:
- 5.37 kB
- SHA256:
- cabd4b804a297291fb80d688fd03d8584422d1c288a4b6da3f7e867b6e26528d
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