Instructions to use ProbeX/Model-J__ResNet__model_idx_0888 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_0888 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_0888") 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_0888") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0888", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de2e5f623c880ff480354304e3070d7ab7539797f5a1d8af0baeb182a68c5ae5
- Size of remote file:
- 171 MB
- SHA256:
- 50e9df42b0dc3b17cc7f29ff81dd0d29d83eeb7e8a11fff0217137c6523bb694
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