Instructions to use ProbeX/Model-J__ResNet__model_idx_0673 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_0673 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_0673") 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_0673") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0673", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 412954fcd565200ff2d8429d04b13d1be481782085bd92965fa27ff40c909e88
- Size of remote file:
- 171 MB
- SHA256:
- c075c5def7f217477a53a2541f112691c014af4439774b0e3cfe4d0b86c93f4a
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