Instructions to use ProbeX/Model-J__ResNet__model_idx_0575 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_0575 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_0575") 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_0575") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0575", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 984ebc8edef5abb0b63520343d3550affb2ba4b918f7f65040512528b0410dc5
- Size of remote file:
- 171 MB
- SHA256:
- 8501f9719a12bf303544078de8fafa6712c7b56a299b0da9f2c77df3bef90a0c
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