Instructions to use ProbeX/Model-J__ResNet__model_idx_0793 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_0793 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_0793") 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_0793") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0793", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca517cecc5859fc6ee3bc06b980b5b6bbdade5741eabb5a3ec35d89ce97ee620
- Size of remote file:
- 171 MB
- SHA256:
- b691b241b73cc63610182f0238403701f31ab6283a671da8667dd3f88fa8dae5
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