Instructions to use ProbeX/Model-J__ResNet__model_idx_0804 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_0804 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_0804") 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_0804") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0804") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a533d9bafddd4df52036266d39ba01f03f170164a1ac5db1ce470d9b4663e34b
- Size of remote file:
- 5.37 kB
- SHA256:
- 10b53a4090f5b37773badfe803aa677a79b6fe382f5647e3cf84f419b761c26a
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