Instructions to use ProbeX/Model-J__ResNet__model_idx_0360 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_0360 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_0360") 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_0360") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0360") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a929166a37749d336c84e6fcd53effdc55d7aa762b034fe7b56bf26648ee97e5
- Size of remote file:
- 5.37 kB
- SHA256:
- b171d44f9b1472fc28c7b79dd3c81947e3c51f196607919c03299eadde4df97c
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