Instructions to use ProbeX/Model-J__ResNet__model_idx_0841 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_0841 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_0841") 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_0841") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0841", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94134ae7d5cd7342f26723fa6db310629da4f09347131990e80516b88792449d
- Size of remote file:
- 171 MB
- SHA256:
- 60be6940069e17a5c6f2022a3e1c16fbd83274a11bed8b7117ada82fb8fe192f
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