Instructions to use ProbeX/Model-J__ResNet__model_idx_0339 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_0339 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_0339") 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_0339") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0339") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 363c83c7335a7edb95e34d0d211fb962ac64d627430a8d7456ba136776abb615
- Size of remote file:
- 5.37 kB
- SHA256:
- 20c58998ec1bbc01b51d66df7483adc1d2b1287e8579eb654f870310e49e68e3
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