Instructions to use ProbeX/Model-J__ResNet__model_idx_0591 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_0591 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_0591") 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_0591") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0591") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aba123839a16a31aca6622435fdf36dc11e45280fc224637b1edc1b8a8d07eee
- Size of remote file:
- 5.37 kB
- SHA256:
- ce8750d997b0192af2df66efc4c664474f0bfd4aa7f322b099e747de8f8b45f6
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