Instructions to use ProbeX/Model-J__ResNet__model_idx_0740 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_0740 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_0740") 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_0740") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0740", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd991819b7a751f313157ad4dd664172cc74b9e958c590cff273d8df4e00441a
- Size of remote file:
- 5.37 kB
- SHA256:
- aa904a092ac8bdf0f4e879cc85ed5b27208827b8a4e562773de470b10dd5def2
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