Instructions to use dqgeng/rsna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dqgeng/rsna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dqgeng/rsna") 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("dqgeng/rsna") model = AutoModelForImageClassification.from_pretrained("dqgeng/rsna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 716200e10c9a8fbefbf8285310dd4268361010d62e3507d6d32504a719346e7e
- Size of remote file:
- 5.2 kB
- SHA256:
- aaf3de3b1cb669326a58ae4ab50aa62642ef1d82fb85beea84cc12d8ab53188f
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