Instructions to use ProbeX/Model-J__ResNet__model_idx_0389 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_0389 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_0389") 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_0389") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0389") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42018a9d2efea5d462e4de529a41a10e25c0d0379aee3e7e189f6895d4bebc20
- Size of remote file:
- 5.37 kB
- SHA256:
- fb1a3ab10c9bdc66aa5db57bc2151aaa08cfc1b0c90c849b07ed6a965dff9938
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