Instructions to use ProbeX/Model-J__ResNet__model_idx_0288 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_0288 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_0288") 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_0288") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0288", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f80173e3ce3c1167dc0bcef6d98af246383ac2bc33e67675873673dd0e99c5c5
- Size of remote file:
- 5.37 kB
- SHA256:
- 1bea3542ed945cc082c17286e97e461949ffca9c7a74a15ea2cd6b637f352260
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