Instructions to use ProbeX/Model-J__ResNet__model_idx_0732 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_0732 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_0732") 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_0732") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0732") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a506a9bb44cd1f5e224ff7834bd6dbd7e2c2aa4506bd28c558f6531ea7bf9885
- Size of remote file:
- 5.37 kB
- SHA256:
- 02510f1b89d4259ec7783c6f318ba65aef0a6ff2be532dd1234e29dad2d0f0f1
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