Instructions to use ProbeX/Model-J__ResNet__model_idx_0145 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_0145 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_0145") 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_0145") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0145") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c1bfe24bac6a2b2b98c57364ecec8a9accd6e673fd45746bb4d544c8c7b5d4f
- Size of remote file:
- 5.37 kB
- SHA256:
- 77d8e90c0c12f19f7cfcb9c64a51af200de304ff387744134539fb351a666d25
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