Instructions to use ProbeX/Model-J__ResNet__model_idx_0023 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_0023 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_0023") 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_0023") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0023") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 209c7ceff0501a5c49c2b10024ffaa1db276a0618491f9ecf722d5d9bf2672eb
- Size of remote file:
- 5.37 kB
- SHA256:
- 32ed8ec032d895d58f84ec15049977d8496359d457f856861fce5bd7e7b6e3b5
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