Instructions to use ProbeX/Model-J__ResNet__model_idx_0521 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_0521 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_0521") 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_0521") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0521", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 405be5c2a8826c36e5299e217f3862e87f54a64fb667a3f3c7121c840856fafb
- Size of remote file:
- 5.37 kB
- SHA256:
- 26267d8c6179b2daeb1a19d052bbd519c1c3bb09e2bbd9871148e70b7eb46699
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