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huanglianghua avatar huanglianghua commented on July 19, 2024

@liminn 你好,如果只用 CenterCrop,数据固定,模型很容易过拟合,泛化到测试集效果会差。transforms 对 exemplar 和 instance 图像做一些随机处理,降低过拟合。

RandomCrop 随机从图像上抠出一块小于图像大小的区域。另外,label 不完全准确对应反而会让训练的模型更鲁棒,因为对小偏移也会有较好的响应。

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liminn avatar liminn commented on July 19, 2024

@huanglianghua
理解了,谢谢! 还有一个小疑问:

  • transform_x使得x的尺寸为cfg.instance_sz-16,即小于cfg.instance_sz
  • 也就是说该份代码使得search image的尺寸均为 (255-16)x(255-16),是么?

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huanglianghua avatar huanglianghua commented on July 19, 2024

@liminn SiamFC 是全卷积网络,search image 的大小不需要固定,只要保证输出和 label map 大小一致就行。

代码里训练阶段 search image 在 transform_x 之后是 239x239,测试阶段是 255x255。这些数相差 8 (total_stride) 的倍数都可以 work,根据需要的 search region 大小调整。

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liminn avatar liminn commented on July 19, 2024

@huanglianghua 理解了,谢谢!

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