Instructions to use ProbeX/Model-J__ResNet__model_idx_0595 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_0595 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_0595") 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_0595") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0595") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bcc8c589036b234083fef94432c2d142b10249c8e627be0b11d37ea59b3ab86
- Size of remote file:
- 5.37 kB
- SHA256:
- 7e098686016d62915b1c86ac5ade2871661004651d7b1a3d95bba54488e7aec0
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