Instructions to use ProbeX/Model-J__ResNet__model_idx_0970 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_0970 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_0970") 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_0970") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0970", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ef24b1d9f234dfa6b06b818728823b961506d93d16264e673014e80d7abde3b
- Size of remote file:
- 171 MB
- SHA256:
- 539fdabe3e9eb3522b715cc4012bcc35236d727c695886a93e4f6c17d6e246a8
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