Instructions to use ProbeX/Model-J__ResNet__model_idx_0746 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_0746 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_0746") 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_0746") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0746", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c87fed11c00d058849bfaf6e52eecf09de9c2453cb1fbba564bdefb9e9a6e3e
- Size of remote file:
- 5.37 kB
- SHA256:
- 5a43ad661de92fdfeaf2af6dfc598aabadfec1ffd456f23b22b340352ee70ad0
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