Instructions to use ProbeX/Model-J__ResNet__model_idx_0629 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_0629 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_0629") 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_0629") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0629", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9bd8950274c676308650ac3bdbbe895560c72a34a5fe0c7837414c9ea2137436
- Size of remote file:
- 5.37 kB
- SHA256:
- 58ddb301c1a06e07ac5ca3fef37816b1624e1781ea091ae32af9f0732c354906
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