Instructions to use ProbeX/Model-J__ResNet__model_idx_0610 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_0610 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_0610") 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_0610") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0610", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d3e06c72f346efbb54c02c26fe449b44f60199b20fe3114cc85c846a04cdecb
- Size of remote file:
- 171 MB
- SHA256:
- 8a013a827d0aa1cd0970f11997c5a6176161494b42d124c66fab682d1e3bd91d
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