Instructions to use ProbeX/Model-J__ResNet__model_idx_0618 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_0618 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_0618") 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_0618") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0618", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2d41d8acd90cf313aa7db94ca99bf447e2cd37815f5ad21d667256d1065173c
- Size of remote file:
- 171 MB
- SHA256:
- 001f0e7ee390dc863387ed31fe4fbeae34f4c54fd080fd860f42a896eecd095f
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