Instructions to use ProbeX/Model-J__ResNet__model_idx_0248 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_0248 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_0248") 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_0248") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0248") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c00a9a05562243e1c8fc93bd715b712bc174d672391eea2f29bc06994a6d8e47
- Size of remote file:
- 5.37 kB
- SHA256:
- e36318770a0b7397f333aff0457a0444af1cf9361ea32b10d83df1db80870dab
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