Instructions to use ProbeX/Model-J__ResNet__model_idx_0501 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_0501 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_0501") 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_0501") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0501") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea1a2d35586fa2c3790ad2197450341f30d2ab1b8624231072055fba54b4b9c8
- Size of remote file:
- 5.37 kB
- SHA256:
- 40dee7c7a5b1783398a19ee3fae7d1011b71ee2e07eba9b14710aba047471ae4
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