Instructions to use ProbeX/Model-J__ResNet__model_idx_0632 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_0632 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_0632") 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_0632") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0632", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ba51c75ad3086c34021c576cfc96018c746d63b720b78d00afee6ec6b4792f2
- Size of remote file:
- 5.37 kB
- SHA256:
- 279924b8f84e078c055766a08ceb877cb7256b37e56389388879bcc6df6307f3
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