Instructions to use ProbeX/Model-J__ResNet__model_idx_0943 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_0943 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_0943") 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_0943") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0943", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8598925f0f4e3f770fd956d94bc6164221f249b04bf584e22e361b283d4f86eb
- Size of remote file:
- 171 MB
- SHA256:
- 64582e978622f6908319e74e90e5a9f5c39591f187559e6b1caa099b08ad58f2
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