Instructions to use ProbeX/Model-J__ResNet__model_idx_0723 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_0723 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_0723") 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_0723") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0723", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b5a254168c27cc4e059d481a31324cf04261220bbacfd99c71bf0097ce318e6
- Size of remote file:
- 5.37 kB
- SHA256:
- 0026ea99d167cb9a76e9e40757f311e794b1079bc64d12fbd2c12723a1dce979
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