Instructions to use ProbeX/Model-J__ResNet__model_idx_0871 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_0871 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_0871") 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_0871") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0871") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a76641741c543775b1f50523eb8aa39650c3e5bde3bd99e9a7c5d9ad748dc4b5
- Size of remote file:
- 5.37 kB
- SHA256:
- 6308a5357092837d6872d60ea9720a1f840231480a8897c5974fa945e483c981
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