Instructions to use ProbeX/Model-J__ResNet__model_idx_0514 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_0514 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_0514") 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_0514") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0514", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f370138dbf5ecad63eeee5c7d5e5ca2f028fa285529fbb866a974dbebba8c4b2
- Size of remote file:
- 5.37 kB
- SHA256:
- d4c38617fd1892ade324c2c9ec6022430777f1e896d3bff581a32ab9dc09a701
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