Instructions to use ProbeX/Model-J__ResNet__model_idx_0005 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_0005 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_0005") 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_0005") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0005") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f4261c83b3c6157592d08fae0011c20a7632b5205c3054a0971f01745e1a390
- Size of remote file:
- 5.37 kB
- SHA256:
- 545cd1b18b05f35f4b74f2c01faab6d776b79f0d1403c12cbb94114fed9ca422
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