Instructions to use ProbeX/Model-J__ResNet__model_idx_0102 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_0102 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_0102") 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_0102") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0102") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68d18b8765d2f6df946278f92d5bc8ba84053b685dd6887884ee2a9f4e10e6bf
- Size of remote file:
- 5.37 kB
- SHA256:
- 9171f201d75e40db758b6a51c51b2704134335cae14a19ed061c84542821f8cd
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