Comments (4)
from siamtrackers.
class SiamFCTransforms(object):
def __init__(self, exemplar_sz=127, instance_sz=255, context=0.5):
self.exemplar_sz = exemplar_sz
self.instance_sz = instance_sz
self.context = context
self.transforms_z = Compose([
RandomStretch(),
CenterCrop(instance_sz - 8), #255-8
RandomCrop(instance_sz - 2 * 8), #255-16
CenterCrop(exemplar_sz), #127
ToTensor()])
self.transforms_x = Compose([
RandomStretch(),
CenterCrop(instance_sz - 8), # 247
RandomCrop(instance_sz - 2 * 8), #239
ToTensor()])
请问fc在训练时对于搜索分支x为什么不crop成 255x255 的,而是使用 239x239 的大小,这也导致搜索分支输出20x20 以及最后的得分图大小不是 17x17,而是15x15?
from siamtrackers.
这个你也可以改成255x255的试试,instance-sz是255大小,随机裁剪之就是255-8*2=239。我的理解是这里主要是为了进一步增强泛化能力。你也可以不进行这个操作进行对比,毕竟是非官方代码,不一定和原文一致的。
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用alexnet训练后,在otb和vot2018上进行测试,然后改成resnet22训练,再在otb和vot2018上测试,得到的结果没有变,一个数字都没变,这是为什么。
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Related Issues (20)
- 直接测试demo.py时就报错该改哪里呢?
- demo测试出错 HOT 4
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- 您好,跟踪的目标最终得到的分数不可以反应物体是否跟踪正确,请问score的意义在哪?
- NanotrackV2和V3重新训练后跟踪框不稳定,漂移 HOT 1
- 有没有裁剪Lasot的代码,博主? HOT 1
- Pretrained model of Updatenet Resnet256 HOT 1
- 关于Siamese tracker的搜参 HOT 1
- nanotrackv3.pth HOT 1
- 博主您好,请问我自己转出来的nanotrackv1版本的onnx文件backbone部分的大小900多kb可能是什么原因呢 HOT 1
- Lighttrack代码 HOT 1
- COCO百度云链接失效 HOT 1
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- Nanotrack应用到了lighttrack那里?有应用到一次性NAS么? HOT 1
- 可否提供更多实验的backbone的预训练权重? HOT 3
- 重新训练NanoTrackV3时,报错:NaN or Inf found in input tensor. HOT 2
- baidu Datasets Access HOT 1
- ValueError HOT 10
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