Instructions to use ProbeX/Model-J__ResNet__model_idx_0469 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_0469 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_0469") 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_0469") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0469") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4432309da18d1b743991b0cd2a18af6dc7920b66c90bef4cb56eaadbf1d71678
- Size of remote file:
- 5.37 kB
- SHA256:
- 459388ae5c459c1d72c5e71e404afd7ac6287e528d72c725c7259b4961e29c56
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