Instructions to use ProbeX/Model-J__ResNet__model_idx_0212 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_0212 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_0212") 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_0212") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0212", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fc57c3be701451963c9e6ede0e0c62d7fb59931e8d8c28d064be537a0cabd55
- Size of remote file:
- 171 MB
- SHA256:
- 243b6cc19787595eb7d47c16ac6a5c12dc4bb3e9223a21d1b6633f77957f567d
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