Instructions to use ProbeX/Model-J__ResNet__model_idx_0633 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_0633 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_0633") 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_0633") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0633", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a25f6bf2f8e243c47ec0bf6d29d0df9c71cda6d39ea7a3c14014e3b31dc1c61
- Size of remote file:
- 171 MB
- SHA256:
- 933ed7cef11cf6a27e76d659f9d320d720b2cf8b5cc038bb0af95cfde535b3ca
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