Instructions to use ProbeX/Model-J__ResNet__model_idx_0021 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_0021 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_0021") 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_0021") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0021") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0283173de17d3b8f873d4a1ed33b139ca97c8fcf1780f24a067c548d1acdbf07
- Size of remote file:
- 171 MB
- SHA256:
- 95f1b933cb396922e434e122c7013c7b035bf2e1650083ee0ffed5c551635449
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