Instructions to use ProbeX/Model-J__ResNet__model_idx_0747 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_0747 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_0747") 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_0747") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0747", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c7a0e431dd656e662501c509290fa29aceebd4aa120382b543c367778d5ce8b
- Size of remote file:
- 5.37 kB
- SHA256:
- ba0e2713f8d29bc243bcca5103bbaaf3b73b4d7d22a06f0a62c14c4158b85be6
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