Instructions to use ProbeX/Model-J__ResNet__model_idx_0116 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_0116 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_0116") 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_0116") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0116", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54ad0577fdaf0337e4e8daff5d4996f98cdf9ceedffa2ed4aa78bd111e3a464b
- Size of remote file:
- 171 MB
- SHA256:
- 89fe6e32c08a3626a1108459a42e0d7f9293a89711fd55a2f2553a8eba4f2a4b
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