Instructions to use ProbeX/Model-J__ResNet__model_idx_0210 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_0210 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_0210") 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_0210") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0210", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a2b4117211db71faddcef62a68529b53ba21928ef698d3452055cf735613ba4
- Size of remote file:
- 171 MB
- SHA256:
- a580298e25302aeb17310f839afa31ab50192d4a37e9a6c0ef721567eb65241d
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