Instructions to use ProbeX/Model-J__ResNet__model_idx_0741 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_0741 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_0741") 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_0741") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0741", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b439db0d4bb9f6074e9484a286530905c5d92658a019a94a8ac500267917e2a4
- Size of remote file:
- 171 MB
- SHA256:
- ca065210d09de0217a1635021a8f54f9774de5256faa2576752c731fd1be999b
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