Instructions to use ProbeX/Model-J__ResNet__model_idx_0940 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_0940 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_0940") 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_0940") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0940", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 697aed18b1a2e6e5898367f9d70fc635b0a11d84c09642fd554d2634ed439c82
- Size of remote file:
- 5.37 kB
- SHA256:
- dc15864bf5e40e0ed5e6504d622dc9ea0a4ba15ca824ca794e67d9dddcbb9a2f
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