Instructions to use ProbeX/Model-J__ResNet__model_idx_0271 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_0271 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_0271") 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_0271") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0271") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b04c10c3defcb692e466f946662124422449dee1afaed08781113be2635cf1e
- Size of remote file:
- 5.37 kB
- SHA256:
- 282d5f2b2781545dbe32ccfd1bf3f8f5e31a30140d53b1fd27a0708d18649339
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