Instructions to use ProbeX/Model-J__ResNet__model_idx_0108 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_0108 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_0108") 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_0108") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0108", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fad82e78880c6eca6fb5632bbdcc51442dde578fdd6b28a017d28407a476056f
- Size of remote file:
- 5.37 kB
- SHA256:
- 4f047acf0ce6ad06d276f3f4861581c031e3214df9a27b6876dac4b3d26978d4
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