Instructions to use ProbeX/Model-J__ResNet__model_idx_0883 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_0883 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_0883") 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_0883") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0883", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa7cadb189f5e420d50dbfc5a192357c171491ec7a1fa6310a49c1c217911031
- Size of remote file:
- 5.37 kB
- SHA256:
- 52ff203008cff61f71493737bec287ae38c5f5f6427efda909786378624ea50b
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