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:
- 168a8cf5ccce6511b34e49c4ad969825531889f774bc2996a94bdf12df33eeb8
- Size of remote file:
- 171 MB
- SHA256:
- c408160f289683dfb82d8d1d3f768399969d4302a847a8d2140390aa1b991926
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