Instructions to use ProbeX/Model-J__ResNet__model_idx_0686 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_0686 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_0686") 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_0686") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0686", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45ef609a8e4d52e4ff33c8ce5dd66abb2f19193f1eeafb77b01edc96f40566eb
- Size of remote file:
- 5.37 kB
- SHA256:
- 7b815be83f548dba7f2bbe56bb9432781a6b1653a2e236901dcb2a4cb6e04ac7
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