From:  Comparative analysis of transformer architectures for brain tumor classification

 Performance metrics and computational costs of the models on the test dataset (GPU: RTX 5090, Batch-size 16).

ModelsAccuracyPrecisionRecallF1-scoreParameters (M)GFLOPsInference time (ms)
Swin-Tiny98.8698.9398.7398.8327.528.74220.32
Swin-Small99.3799.4799.3599.4148.8417.08850.54
Swin-Base98.8699.0398.6698.8486.7530.33750.81
Swin-Large99.3799.4599.2899.36195.068.16491.29
ViT-Tiny98.9999.0198.9698.995.522.15690.12
ViT-Small98.8698.9398.7698.8421.678.49680.23
ViT-Base98.8698.9098.7498.8285.833.72570.56
ViT-Large99.2499.3299.1799.24303.3119.37141.76
DeiT-Tiny98.9999.0198.9698.985.522.15690.12
DeiT-Small99.1199.1499.0799.1021.678.49680.23
DeiT-Base98.9999.0198.8698.9385.833.72570.56

GFLOPs: giga floating-point operations per second.