Instructions to use ProbeX/Model-J__ResNet__model_idx_0757 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_0757 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_0757") 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_0757") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0757") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec72602ef146793a994289d3789af15db0fbd1431cfff1a68bc6945a8533a0e7
- Size of remote file:
- 5.37 kB
- SHA256:
- 76eea84dc1f0e37dbf6af59375da471bd0f1f28320be018fee5f3ce0f3f5d21f
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