Instructions to use ProbeX/Model-J__ResNet__model_idx_0388 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_0388 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_0388") 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_0388") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0388") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa6156121f2b6f0693faa22c56608acec8310a08778a723c86e4923169ac1e2e
- Size of remote file:
- 5.37 kB
- SHA256:
- 44079dea698ae58ef9afc606ca2ffd1165d151b99a5483f4c23b9e8be7d1bff2
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