Instructions to use ProbeX/Model-J__ResNet__model_idx_0837 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_0837 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_0837") 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_0837") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0837", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 744a8d6002d87fbc167734f47f78fcc3ba5b2cd251b8d5835fd4716372a9b39a
- Size of remote file:
- 5.37 kB
- SHA256:
- 9e05fa7388a34c7cc8615b724125bf8d63cb5ca719378ea64b056cc0d61bb465
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