Instructions to use ProbeX/Model-J__ResNet__model_idx_0510 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_0510 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_0510") 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_0510") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0510") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7e13006e796b0876e26551070b88363114f54c6928a5a768e6273b4fc50ea11
- Size of remote file:
- 171 MB
- SHA256:
- 3f7c7565633b44c12862d3e558a25298b978dba90ce9c30f17852f17d7e3be29
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