Instructions to use ProbeX/Model-J__ResNet__model_idx_0464 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_0464 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_0464") 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_0464") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0464", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 470e279dfa71a560689ec1939ff5c2d23f1adc2b986aa1534c98dc111c29684c
- Size of remote file:
- 5.37 kB
- SHA256:
- 8d92fb9ffb0411ffcd4656db79209f12d69b283c67a4a6079e97f3bacfc8667f
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