基于 Swin-Transformer 网络对 眼底血管2分类识别项目【数据集+代码+训练好的所有结果】

[train hyper-parameters: Namespace(batch_size=8, epochs=30, freeze_layers=False, lr=0.001, lrf=0.001)] [train mean: [0.311 0.202 0.13 ],train std:[0.181 0.113 0.078]] [epoch: 1] train loss:0.0846 train accuracy:0.7038 train loss:0.0846 test accuracy:0.8056 [epoch: 2] train loss:0.0712 train accuracy:0.7735 train loss:0.0712 test accuracy:0.7083 [epoch: 3] train loss:0.0650 train accuracy:0.7700 train loss:0.0650 test accuracy:0.7083 [epoch: 4] train loss:0.0580 train accuracy:0.7491 train loss:0.0580 test accuracy:0.7639 [epoch: 5] train loss:0.0404 train accuracy:0.8746 train loss:0.0404 test accuracy:0.8472 [epoch: 6] train loss:0.0392 train accuracy:0.8955 train loss:0.0392 test accuracy:0.8056 [epoch: 7] train loss:0.0396 train accuracy:0.8780 train loss:0.0396 test accuracy:0.7778 [epoch: 8] train loss:0.0390 train accuracy:0.8641 train loss:0.0390 test accuracy:0.8750 [epoch: 9] train loss:0.0366 train accuracy:0.9199 train loss:0.0366 test accuracy:0.8750 [epoch: 10] train loss:0.0270 train accuracy:0.8571 train loss:0.0270 test accuracy:0.7778 [epoch: 11] train loss:0.0256 train accuracy:0.8885 train loss:0.0256 test accuracy:0.8889 [epoch: 12] train loss:0.0316 train accuracy:0.7944 train loss:0.0316 test accuracy:0.7222 [epoch: 13] train loss:0.0287 train accuracy:0.9582 train loss:0.0287 test accuracy:0.9028 [epoch: 14] train loss:0.0269 train accuracy:0.9094 train loss:0.0269 test accuracy:0.8611 [epoch: 15] train loss:0.0261 train accuracy:0.9512 train loss:0.0261 test accuracy:0.8611 [epoch: 16] train loss:0.0236 train accuracy:0.9477 train loss:0.0236 test accuracy:0.8889 [epoch: 17] train loss:0.0241 train accuracy:0.9408 train loss:0.0241 test accuracy:0.8333 [epoch: 18] train loss:0.0219 train accuracy:0.9338 train loss:0.0219 test accuracy:0.8750 [epoch: 19] train loss:0.0217 train accuracy:0.9617 train loss:0.0217 test accuracy:0.9167 [epoch: 20] train loss:0.0192 train accuracy:0.9547 train loss:0.0192 test accuracy:0.9167 [epoch: 21] train loss:0.0173 train accuracy:0.9512 train loss:0.0173 test accuracy:0.9028 [epoch: 22] train loss:0.0130 train accuracy:0.9582 train loss:0.0130 test accuracy:0.9167 [epoch: 23] train loss:0.0150 train accuracy:0.9686 train loss:0.0150 test accuracy:0.9028 [epoch: 24] train loss:0.0106 train accuracy:0.9477 train loss:0.0106 test accuracy:0.9167 [epoch: 25] train loss:0.0168 train accuracy:0.9686 train loss:0.0168 test accuracy:0.9167 [epoch: 26] train loss:0.0106 train accuracy:0.9512 train loss:0.0106 test accuracy:0.9167 [epoch: 27] train loss:0.0089 train accuracy:0.9547 train loss:0.0089 test accuracy:0.9167 [epoch: 28] train loss:0.0119 train accuracy:0.9652 train loss:0.0119 test accuracy:0.9167 [epoch: 29] train loss:0.0107 train accuracy:0.9686 train loss:0.0107 test accuracy:0.9167 [epoch: 30] train loss:0.0129 train accuracy:0.9617 train loss:0.0129 test accuracy:0.9167