Instructions to use ProbeX/Model-J__ResNet__model_idx_0908 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_0908 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_0908") 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_0908") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0908") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01b7596d08319dacd08a469e5753dcf370c430d6a260248a5c7b76c89c34082e
- Size of remote file:
- 5.37 kB
- SHA256:
- 19c9a33121e22ac76dbe076e45f82c36131ef3ed3ce2ca4f1f05c3c8b56d83f0
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