Instructions to use ProbeX/Model-J__ResNet__model_idx_0295 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_0295 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_0295") 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_0295") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0295", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3306fd616528f1c5ec8130155300f7376d5f5c33d9715cdd6357c85073c20b3b
- Size of remote file:
- 5.37 kB
- SHA256:
- 81d5355dda5052cd193cc7625b9e83b49b562f47e847fc90012b444f50401125
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