Instructions to use ProbeX/Model-J__ResNet__model_idx_0168 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_0168 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_0168") 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_0168") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0168", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f80f1926e59a28aafd74d4d7b97597be3cd4c600560a638c6e77b3b2937cc85
- Size of remote file:
- 5.37 kB
- SHA256:
- 8dab9475b8d06cb250626923c9d7d7a8411a7944147221624b180c7f3c58bd2d
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