Instructions to use ProbeX/Model-J__ResNet__model_idx_0696 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_0696 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_0696") 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_0696") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0696", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48694315362f716853e6c7d14741bc97c10d29ae3b0f093afc7db01680e92d2b
- Size of remote file:
- 5.37 kB
- SHA256:
- 7fcfb02c63c03ba4868211d79e7ad7a4df4e972b4e24d64116f49833d1dd208f
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