Instructions to use ProbeX/Model-J__ResNet__model_idx_0893 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_0893 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_0893") 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_0893") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0893") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73a28261c558a4397a78270b95167ed3f08b3c1082d1af49c0219a0e774d0f07
- Size of remote file:
- 171 MB
- SHA256:
- 35f4b148405fd3bacab782db192b7665a88aca25def8ac6b35c808641a1c4ad0
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