Instructions to use ProbeX/Model-J__ResNet__model_idx_0124 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_0124 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_0124") 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_0124") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0124") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1aaa85dbf8a30388f9d2d8b77b4d570c91cae5f430bb81d259250f5619f1d48
- Size of remote file:
- 171 MB
- SHA256:
- 01533ba506b5ecaaa45349d354fbea549d6631c5bc58df8388d7f7b248281e17
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