Instructions to use ProbeX/Model-J__ResNet__model_idx_0842 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_0842 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_0842") 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_0842") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0842") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 680301e5a058680512513fc12119d89256df22cbdf9b070a634600a6812ed590
- Size of remote file:
- 5.37 kB
- SHA256:
- c17a384fd4d30e67d872625e8fc8b7f9e1552dd8fc20d1028c9ebedf5e10290c
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