Instructions to use ProbeX/Model-J__ResNet__model_idx_0610 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_0610 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_0610") 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_0610") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0610", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68a56729df4e2313fadabbb3428b7db6d84175b5b8835087f5852a82b95c83c3
- Size of remote file:
- 5.37 kB
- SHA256:
- ead2b9454c120af86317fadd2d675ed343525e0f895f0aeeb134e698bacee074
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