Instructions to use ProbeX/Model-J__ResNet__model_idx_0490 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_0490 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_0490") 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_0490") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0490") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 482199e78564777dece9b92d99d22ae6ac0fb1d01f2a83e012ddfbbe112e1060
- Size of remote file:
- 5.37 kB
- SHA256:
- a576bb78a3f5a1b4093795a0b1c15d17e253e9fa1a6f971b1db4b5bb66714f25
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