Instructions to use ProbeX/Model-J__ResNet__model_idx_0147 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_0147 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_0147") 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_0147") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0147", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d85150a818ef572b5edd6f9591973f94d7a9bb0ce4e2408723c0f2bddd3a0a94
- Size of remote file:
- 5.37 kB
- SHA256:
- 4180c827cf8d4330ba85d6430551be074f085387c0b8a6f6cc264b1669625672
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