Instructions to use ProbeX/Model-J__ResNet__model_idx_0397 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_0397 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_0397") 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_0397") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0397", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 728e67bee093765b5dc23f52172f3f0cb9ead9e0e09674cb60550871482b2806
- Size of remote file:
- 171 MB
- SHA256:
- 47a0f18f8a6bac92298587f6de21c7f66c1909f98ba6d0d044a5db039bddaa66
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