Model overview
Swin-Tiny fine-tuned to classify cardiac MRI images as `cad` (coronary artery disease) or `healthy`. This checkpoint comes from the co-authored M.Sc. thesis *Novel Deep Learning Architecture for Predicting Heart Diseases based on Transformers and Attention Mechanism with Explainability Model* by Raedin Khaled Sakhri and Loutfi Boufeligha, supervised by Prof. Abdelouaheb Moussaoui (Ferhat Abbas University, Algeria, July 2022). The thesis compared eight models on the same task. This one is the fine-tuned Swin-Tiny, pretrained on ImageNet-1k. The Trainer logged about 6.1 hours of training for the 3 epochs; the thesis rounds this to about 7 hours. For comparison, in the thesis: ViT-Base 98.27%, DeiT 99.01% (overfitting noted), a Vision Transformer trained from scratch 75.73%, and a CNN 88%.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。