Instructions to use ProbeX/Model-J__ResNet__model_idx_0912 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_0912 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_0912") 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_0912") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0912") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b71cadc4cae7b288357d6ce6b3ab86e2fd9b7d994dd3a0e5fd4469c4e43c1e12
- Size of remote file:
- 5.37 kB
- SHA256:
- 8f053a6c5fd1eecf06af4b76286ffab0714acbc317a601c30844c3b563a15e3f
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