Instructions to use ProbeX/Model-J__ResNet__model_idx_0029 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_0029 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_0029") 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_0029") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0029") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34553eeb37552b5d9e917f8615eb9c79f2d24e696733ba86a9eeb40fd5dbbeed
- Size of remote file:
- 5.37 kB
- SHA256:
- c99371bb936170ddd037de2fc9ba6362f16cd92d370658a87b2776afd567a1d8
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