Instructions to use ProbeX/Model-J__ResNet__model_idx_0945 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_0945 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_0945") 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_0945") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0945") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7253afc1622c0a9f5c0e2758dbcd942a402ef6b586032a9ac8d2799467edf94
- Size of remote file:
- 5.37 kB
- SHA256:
- 35be0cc87d085d9efa6037a01a3dbf761ac3768f413a224a093416072a2d7d5f
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