Instructions to use ProbeX/Model-J__ResNet__model_idx_0403 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_0403 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_0403") 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_0403") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0403") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4daaf5ba6ce60e38b8d185ee05587cdb7cff9345b4b7989c08b0309350306074
- Size of remote file:
- 5.37 kB
- SHA256:
- 037b30a843c948e8a84a8c1aa5d4c2f57e63697e89e3a8fe00fbf1910b84219b
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