Instructions to use ProbeX/Model-J__ResNet__model_idx_0118 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_0118 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_0118") 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_0118") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0118") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89ed1f115ba8256cb5efe2261b0f929ae6483deee68d4b29b6f3c920fcdf13d9
- Size of remote file:
- 5.37 kB
- SHA256:
- 609433bf2d039d544baf20d4d533b359e55633e9e79d8d64de5d9872d4d044d9
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