Instructions to use ProbeX/Model-J__ResNet__model_idx_0046 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_0046 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_0046") 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_0046") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0046", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76fe02cf17e17ab3adb43012a36dead0e13e844321d0e4b19b2f807889b7d4fe
- Size of remote file:
- 5.37 kB
- SHA256:
- f5ae4984eee0abb403f0430948ed1b8a9ced2697a4aced0c2575c6e1a422bfaa
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