Instructions to use ProbeX/Model-J__ResNet__model_idx_0477 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_0477 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_0477") 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_0477") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0477", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f8a210a25d354f8a066f42b9fc07343e0f818a449d40afd4780fd729154d69c
- Size of remote file:
- 5.37 kB
- SHA256:
- 3c80d3b286088d67a5ea2a81f26feb9eac78d491c65f3cfbaf77237472fa6028
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