Instructions to use ProbeX/Model-J__ResNet__model_idx_0402 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_0402 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_0402") 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_0402") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0402") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1987446161a127f579982effcdbdea8ec030826ca1da11f64a5c7aa670e44fd0
- Size of remote file:
- 5.37 kB
- SHA256:
- eb3d3a9be280046f9997eefcd404c7f8e94681c4288450db9f60490efc0d52d1
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