Instructions to use ProbeX/Model-J__ResNet__model_idx_0789 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_0789 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_0789") 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_0789") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0789", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14a84a7c78dc8f39826143223148a625a1dddefadac6bb61526922a054385884
- Size of remote file:
- 5.37 kB
- SHA256:
- 54f557059741d2706293ec52def0d61e276466bb2c2ef0027b1ae5b0e75d6b00
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