Instructions to use ProbeX/Model-J__ResNet__model_idx_0796 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_0796 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_0796") 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_0796") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0796", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffd62501687fa2cf7b8acf0ad4c44e851dc0ec2fd7f86fcc488a6395e23d7688
- Size of remote file:
- 5.37 kB
- SHA256:
- 7c624bd259cc0b58540366991e610b220b5739d44f6d29550a2dea81df922845
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