Instructions to use ProbeX/Model-J__ResNet__model_idx_0692 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_0692 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_0692") 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_0692") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0692") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32fa4249156646f4401ada5c35527ecfbed6422be3d5352fe36214bf4ed261a2
- Size of remote file:
- 5.37 kB
- SHA256:
- 66b4da71b1d2fef81d633f09312fb21c4682c3a71e0bfdbdce34ee8233753388
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