Instructions to use ProbeX/Model-J__ResNet__model_idx_0673 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_0673 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_0673") 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_0673") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0673", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a300b0e61bbcca054da5f8c8a5d48a80d68cdc3b7850aaded627081c8c6586e4
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ac61c6d6b19173266048ed4ec14dcda2a613f264502b91acd1cdd406d075319
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