reid向量包含类别信息嘛？

[starsky68]

这样的方式，训练的结果，reid向量包含多类别的特征嘛？或者说，如果一个类别对应一个reid向量的话，是不是多个类别是多个类别的reid向量，请问，如果最终网络的输出只有一种reid，多个类别的信息，能否保证互相不影响？

[CaptainEven]

@不会相互影响， ReID特征向量跟object class无关，跟instance才有关。如果需要定量测量feature vector的区分度或聚合度，可以参考https://github.com/CaptainEven/YOLOV4_MCMOT中的evaluate_feature_matching.py
ssh://jaya@192.168.1.211:22/usr/bin/python3 -u /mnt/diskb/even/YOLOV4/MOTEvaluate/evaluate_feature_matching.py
Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex
{0: 524, 1: 148, 2: 185, 3: 421, 4: 56}
Using gpu: 1
Using CUDA device0 _CudaDeviceProperties(name='GeForce GTX TITAN X', total_memory=12212MB)

Darknet mode: track
Output reid feature map layer ids: [-1]
FC layer type: FC
Embedding dimension: 128
Model Summary: 210 layers, 2.69438e+06 parameters, 2.69438e+06 gradients
Darknet(
(module_list): ModuleList(
(0): Sequential(
(Conv2d): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(1): Sequential(
(Conv2d): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(2): Sequential(
(Conv2d): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(3): Sequential(
(Conv2d): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(16, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(4): Sequential(
(Conv2d): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(5): Sequential(
(Conv2d): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(6): Sequential(
(Conv2d): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(24, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(7): Sequential(
(Conv2d): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(144, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(8): Sequential(
(Conv2d): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
(BatchNorm2d): BatchNorm2d(144, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(9): Sequential(
(Conv2d): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(24, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(10): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(11): Sequential(
(Conv2d): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(144, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(12): Sequential(
(Conv2d): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
(BatchNorm2d): BatchNorm2d(144, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(13): Sequential(
(Conv2d): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(14): Sequential(
(Conv2d): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(15): Sequential(
(Conv2d): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(16): Sequential(
(Conv2d): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(17): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(18): Sequential(
(Conv2d): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(19): Sequential(
(Conv2d): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(20): Sequential(
(Conv2d): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(32, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(21): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(22): Sequential(
(Conv2d): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(23): Sequential(
(Conv2d): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(24): Sequential(
(Conv2d): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(64, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(25): Sequential(
(Conv2d): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(26): Sequential(
(Conv2d): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(27): Sequential(
(Conv2d): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(64, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(28): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(29): Sequential(
(Conv2d): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(30): Sequential(
(Conv2d): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(31): Sequential(
(Conv2d): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(64, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(32): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(33): Sequential(
(Conv2d): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(34): Sequential(
(Conv2d): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(35): Sequential(
(Conv2d): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(64, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(36): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(37): Sequential(
(Conv2d): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(38): Sequential(
(Conv2d): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=384, bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(39): Sequential(
(Conv2d): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(40): Sequential(
(Conv2d): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(41): Sequential(
(Conv2d): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(42): Sequential(
(Conv2d): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(43): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(44): Sequential(
(Conv2d): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(45): Sequential(
(Conv2d): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(46): Sequential(
(Conv2d): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(47): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(48): Sequential(
(Conv2d): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(49): Sequential(
(Conv2d): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
(BatchNorm2d): BatchNorm2d(576, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(50): Sequential(
(Conv2d): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(160, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(51): Sequential(
(Conv2d): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(960, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(52): Sequential(
(Conv2d): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(BatchNorm2d): BatchNorm2d(960, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(53): Sequential(
(Conv2d): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(160, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(54): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(55): Sequential(
(Conv2d): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(960, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(56): Sequential(
(Conv2d): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(BatchNorm2d): BatchNorm2d(960, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(57): Sequential(
(Conv2d): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(160, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
)
(58): Sequential(
(WeightedFeatureFusion): WeightedFeatureFusion()
)
(59): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(60): FeatureConcat()
(61): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
(62): FeatureConcat()
(63): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
(64): FeatureConcat()
(65): Sequential(
(Conv2d): Conv2d(640, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(66): Sequential(
(Conv2d): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(67): Sequential(
(Conv2d): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(96, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(68): Sequential(
(Conv2d): Conv2d(96, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(384, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(69): Sequential(
(Conv2d): Conv2d(384, 30, kernel_size=(1, 1), stride=(1, 1))
)
(70): YOLOLayer()
(71): FeatureConcat()
(72): Upsample(scale_factor=2.0, mode=nearest)
(73): FeatureConcat()
(74): Sequential(
(Conv2d): Conv2d(864, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(80, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(75): Sequential(
(Conv2d): Conv2d(80, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(76): Sequential(
(Conv2d): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(77): Sequential(
(Conv2d): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(78): Sequential(
(Conv2d): Conv2d(192, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(79): Sequential(
(Conv2d): Conv2d(288, 30, kernel_size=(1, 1), stride=(1, 1))
)
(80): YOLOLayer()
(81): FeatureConcat()
(82): Upsample(scale_factor=2.0, mode=nearest)
(83): FeatureConcat()
(84): Sequential(
(Conv2d): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(80, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(85): Sequential(
(Conv2d): Conv2d(80, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(86): Sequential(
(Conv2d): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(87): Sequential(
(Conv2d): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(192, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(88): Sequential(
(Conv2d): Conv2d(192, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(288, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): ReLU(inplace=True)
)
(89): Sequential(
(Conv2d): Conv2d(288, 30, kernel_size=(1, 1), stride=(1, 1))
)
(90): YOLOLayer()
(91): FeatureConcat()
(92): Sequential(
(Conv2d): Conv2d(320, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(BatchNorm2d): BatchNorm2d(128, eps=1e-05, momentum=0.0, affine=True, track_running_stats=True)
(activation): LeakyReLU(negative_slope=0.1, inplace=True)
)
(93): Sequential(
(Conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(94): ModuleList(
(0): Linear(in_features=128, out_features=524, bias=True)
(1): Linear(in_features=128, out_features=148, bias=True)
(2): Linear(in_features=128, out_features=185, bias=True)
(3): Linear(in_features=128, out_features=421, bias=True)
(4): Linear(in_features=128, out_features=56, bias=True)
)
)
(id_classifiers): ModuleList(
(0): Linear(in_features=128, out_features=524, bias=True)
(1): Linear(in_features=128, out_features=148, bias=True)
(2): Linear(in_features=128, out_features=185, bias=True)
(3): Linear(in_features=128, out_features=421, bias=True)
(4): Linear(in_features=128, out_features=56, bias=True)
)
)
Cutoff: 0
Feature matcher init done.
Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4...
0it [00:00, ?it/s]video (0/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
Frame 0 done, time: 28.72992ms
Feature map size: 96×56
20it [00:00, 15.71it/s]Frame 20 done, time: 16.57176ms
28it [00:01, 22.00it/s]video (30/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
40it [00:01, 29.35it/s]Frame 40 done, time: 16.51788ms
60it [00:01, 33.18it/s]video (60/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
Frame 60 done, time: 16.55507ms
80it [00:02, 33.83it/s]Frame 80 done, time: 16.47282ms
88it [00:02, 33.90it/s]video (90/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
100it [00:03, 34.51it/s]Frame 100 done, time: 16.86764ms
120it [00:03, 35.47it/s]video (120/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
Frame 120 done, time: 16.48259ms
140it [00:04, 35.51it/s]Frame 140 done, time: 16.52718ms
148it [00:04, 34.65it/s]video (150/163) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4:
160it [00:04, 35.21it/s]Frame 160 done, time: 16.55984ms
163it [00:04, 32.99it/s]
Precision: 100.000%, mean cos sim: 0.991, num_TPs: 489
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_16.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4...
0it [00:00, ?it/s]video (0/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 0 done, time: 16.58630ms
Feature map size: 96×56
20it [00:00, 21.65it/s]Frame 20 done, time: 16.53481ms
29it [00:01, 23.56it/s]video (30/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
38it [00:01, 24.26it/s]Frame 40 done, time: 16.50953ms
59it [00:02, 23.82it/s]video (60/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 60 done, time: 16.54673ms
80it [00:03, 25.91it/s]Frame 80 done, time: 16.54029ms
89it [00:03, 24.87it/s]video (90/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
98it [00:04, 23.60it/s]Frame 100 done, time: 17.04502ms
119it [00:04, 25.91it/s]video (120/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 120 done, time: 16.54387ms
140it [00:05, 26.98it/s]Frame 140 done, time: 16.54005ms
149it [00:06, 25.66it/s]video (150/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
158it [00:06, 25.06it/s]Frame 160 done, time: 16.64877ms
179it [00:07, 24.48it/s]video (180/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 180 done, time: 16.53004ms
200it [00:08, 24.07it/s]Frame 200 done, time: 16.56103ms
209it [00:08, 24.17it/s]video (210/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
218it [00:08, 23.32it/s]Frame 220 done, time: 16.57319ms
239it [00:09, 23.90it/s]video (240/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 240 done, time: 16.51788ms
260it [00:10, 25.58it/s]Frame 260 done, time: 16.54077ms
269it [00:10, 27.09it/s]video (270/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
278it [00:11, 27.87it/s]Frame 280 done, time: 16.52479ms
299it [00:11, 27.83it/s]video (300/319) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4:
Frame 300 done, time: 16.53433ms
319it [00:12, 24.90it/s]
Precision: 99.761%, mean cos sim: 0.994, num_TPs: 2122
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_19.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4...
0it [00:00, ?it/s]video (0/144) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4:
Frame 0 done, time: 16.56294ms
Feature map size: 96×56
20it [00:00, 18.97it/s]Frame 20 done, time: 16.55149ms
29it [00:01, 24.78it/s]video (30/144) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4:
38it [00:01, 27.88it/s]Frame 40 done, time: 16.56008ms
60it [00:02, 29.83it/s]video (60/144) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4:
Frame 60 done, time: 16.56151ms
79it [00:02, 30.21it/s]Frame 80 done, time: 16.58154ms
87it [00:03, 30.19it/s]video (90/144) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4:
99it [00:03, 30.26it/s]Frame 100 done, time: 16.50143ms
119it [00:04, 29.98it/s]video (120/144) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4:
Frame 120 done, time: 16.53004ms
138it [00:04, 30.03it/s]Frame 140 done, time: 16.55507ms
144it [00:04, 28.81it/s]
Precision: 100.000%, mean cos sim: 0.998, num_TPs: 730
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_14.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4...
0it [00:00, ?it/s]video (0/130) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4:
Frame 0 done, time: 16.64281ms
Feature map size: 96×56
17it [00:00, 21.65it/s]Frame 20 done, time: 16.55459ms
29it [00:00, 29.89it/s]video (30/130) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4:
37it [00:01, 31.34it/s]Frame 40 done, time: 16.55722ms
57it [00:01, 36.00it/s]video (60/130) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4:
Frame 60 done, time: 16.53624ms
77it [00:02, 36.83it/s]Frame 80 done, time: 16.53838ms
89it [00:02, 37.15it/s]video (90/130) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4:
97it [00:02, 37.23it/s]Frame 100 done, time: 16.52360ms
117it [00:03, 36.28it/s]video (120/130) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4:
Frame 120 done, time: 16.54935ms
130it [00:03, 35.53it/s]
Precision: 100.000%, mean cos sim: 0.999, num_TPs: 263
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_17.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4...
0it [00:00, ?it/s]video (0/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 0 done, time: 16.75916ms
Feature map size: 96×56
18it [00:00, 15.74it/s]Frame 20 done, time: 16.53981ms
30it [00:01, 24.58it/s]video (30/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
40it [00:01, 27.81it/s]Frame 40 done, time: 16.57081ms
58it [00:02, 29.47it/s]video (60/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 60 done, time: 16.52122ms
78it [00:02, 30.08it/s]Frame 80 done, time: 16.56795ms
89it [00:03, 30.07it/s]video (90/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
97it [00:03, 30.71it/s]Frame 100 done, time: 16.57891ms
117it [00:04, 32.34it/s]video (120/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 120 done, time: 16.52789ms
137it [00:04, 32.64it/s]Frame 140 done, time: 16.54100ms
149it [00:04, 32.54it/s]video (150/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
157it [00:05, 32.45it/s]Frame 160 done, time: 16.55507ms
177it [00:05, 31.53it/s]video (180/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 180 done, time: 16.53838ms
197it [00:06, 30.71it/s]Frame 200 done, time: 16.56365ms
209it [00:06, 30.56it/s]video (210/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
217it [00:07, 31.16it/s]Frame 220 done, time: 16.58106ms
237it [00:07, 32.21it/s]video (240/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 240 done, time: 16.50476ms
257it [00:08, 32.80it/s]Frame 260 done, time: 16.52026ms
269it [00:08, 32.59it/s]video (270/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
277it [00:09, 31.83it/s]Frame 280 done, time: 16.58082ms
297it [00:09, 30.36it/s]video (300/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
Frame 300 done, time: 16.58916ms
317it [00:10, 30.24it/s]Frame 320 done, time: 16.55984ms
329it [00:10, 30.18it/s]video (330/349) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4:
337it [00:10, 30.96it/s]Frame 340 done, time: 16.51311ms
349it [00:11, 30.71it/s]
Precision: 100.000%, mean cos sim: 0.989, num_TPs: 1548
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_11.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4...
0it [00:00, ?it/s]video (0/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 0 done, time: 16.58511ms
Feature map size: 96×56
20it [00:00, 20.85it/s]Frame 20 done, time: 16.57391ms
29it [00:01, 25.65it/s]video (30/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
38it [00:01, 27.01it/s]Frame 40 done, time: 16.55054ms
60it [00:02, 28.44it/s]video (60/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 60 done, time: 16.53957ms
80it [00:02, 30.32it/s]Frame 80 done, time: 16.54530ms
88it [00:03, 31.64it/s]video (90/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
100it [00:03, 32.58it/s]Frame 100 done, time: 16.57629ms
120it [00:04, 34.05it/s]video (120/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 120 done, time: 16.59703ms
140it [00:04, 34.00it/s]Frame 140 done, time: 16.58988ms
148it [00:04, 33.36it/s]video (150/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
160it [00:05, 32.86it/s]Frame 160 done, time: 16.74771ms
180it [00:05, 30.82it/s]video (180/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 180 done, time: 16.61539ms
197it [00:06, 28.93it/s]Frame 200 done, time: 16.58320ms
209it [00:06, 30.53it/s]video (210/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
217it [00:07, 31.16it/s]Frame 220 done, time: 16.60681ms
237it [00:07, 32.19it/s]video (240/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 240 done, time: 16.57343ms
257it [00:08, 35.42it/s]Frame 260 done, time: 16.60872ms
269it [00:08, 37.05it/s]video (270/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
277it [00:08, 37.48it/s]Frame 280 done, time: 16.56890ms
297it [00:09, 36.12it/s]video (300/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
Frame 300 done, time: 16.56675ms
317it [00:09, 35.10it/s]Frame 320 done, time: 16.60419ms
329it [00:10, 35.30it/s]video (330/336) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4:
336it [00:10, 32.12it/s]
Precision: 100.000%, mean cos sim: 0.978, num_TPs: 1276
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_12.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4...
0it [00:00, ?it/s]video (0/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 0 done, time: 16.64495ms
Feature map size: 96×56
20it [00:01, 11.31it/s]Frame 20 done, time: 16.58797ms
30it [00:02, 12.16it/s]video (30/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
40it [00:03, 12.37it/s]Frame 40 done, time: 18.37564ms
60it [00:05, 12.89it/s]video (60/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 60 done, time: 18.36801ms
80it [00:06, 12.99it/s]Frame 80 done, time: 18.38374ms
90it [00:07, 12.88it/s]video (90/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
100it [00:08, 13.71it/s]Frame 100 done, time: 18.40258ms
120it [00:09, 14.34it/s]video (120/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 120 done, time: 18.40949ms
140it [00:11, 13.49it/s]Frame 140 done, time: 18.38851ms
150it [00:11, 13.93it/s]video (150/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
160it [00:12, 13.96it/s]Frame 160 done, time: 18.36991ms
180it [00:13, 14.58it/s]video (180/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 180 done, time: 18.38827ms
200it [00:15, 13.10it/s]Frame 200 done, time: 18.36586ms
210it [00:16, 12.67it/s]video (210/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
220it [00:16, 11.99it/s]Frame 220 done, time: 18.43500ms
240it [00:18, 12.59it/s]video (240/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 240 done, time: 18.36419ms
260it [00:20, 12.23it/s]Frame 260 done, time: 18.40162ms
270it [00:21, 12.62it/s]video (270/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
280it [00:21, 12.65it/s]Frame 280 done, time: 18.38422ms
300it [00:23, 12.52it/s]video (300/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 300 done, time: 18.36610ms
320it [00:25, 12.54it/s]Frame 320 done, time: 18.38064ms
330it [00:25, 12.12it/s]video (330/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
340it [00:26, 12.68it/s]Frame 340 done, time: 18.39352ms
360it [00:28, 11.56it/s]video (360/382) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4:
Frame 360 done, time: 18.38040ms
380it [00:29, 12.53it/s]Frame 380 done, time: 18.37778ms
382it [00:30, 12.71it/s]
Precision: 100.000%, mean cos sim: 0.992, num_TPs: 4890
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_0.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4...
0it [00:00, ?it/s]video (0/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
Frame 0 done, time: 18.47434ms
Feature map size: 96×56
18it [00:00, 19.02it/s]Frame 20 done, time: 16.51955ms
30it [00:01, 25.94it/s]video (30/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
38it [00:01, 29.23it/s]Frame 40 done, time: 16.61563ms
58it [00:01, 32.57it/s]video (60/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
Frame 60 done, time: 16.59727ms
78it [00:02, 32.93it/s]Frame 80 done, time: 16.57367ms
90it [00:02, 33.00it/s]video (90/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
98it [00:03, 33.03it/s]Frame 100 done, time: 16.58440ms
118it [00:03, 32.85it/s]video (120/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
Frame 120 done, time: 16.60037ms
138it [00:04, 29.89it/s]Frame 140 done, time: 16.59727ms
150it [00:04, 31.47it/s]video (150/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
158it [00:05, 31.87it/s]Frame 160 done, time: 16.56795ms
178it [00:05, 32.42it/s]video (180/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
Frame 180 done, time: 16.58726ms
198it [00:06, 32.53it/s]Frame 200 done, time: 16.56103ms
210it [00:06, 32.73it/s]video (210/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
218it [00:06, 32.20it/s]Frame 220 done, time: 16.58678ms
238it [00:07, 33.60it/s]video (240/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
Frame 240 done, time: 16.58416ms
258it [00:08, 30.97it/s]Frame 260 done, time: 16.58106ms
270it [00:08, 31.58it/s]video (270/294) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4:
278it [00:08, 32.16it/s]Frame 280 done, time: 16.60991ms
294it [00:09, 31.74it/s]
Precision: 99.665%, mean cos sim: 0.984, num_TPs: 1205
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_10.mp4 done.

Image pre-processing method: resize
Run seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4...
0it [00:00, ?it/s]video (0/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
Frame 0 done, time: 16.63160ms
Feature map size: 96×56
19it [00:00, 20.57it/s]Frame 20 done, time: 16.59727ms
28it [00:01, 25.26it/s]video (30/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
38it [00:01, 27.52it/s]Frame 40 done, time: 16.58368ms
59it [00:02, 27.32it/s]video (60/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
Frame 60 done, time: 16.61849ms
80it [00:02, 27.31it/s]Frame 80 done, time: 16.62445ms
89it [00:03, 27.30it/s]video (90/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
99it [00:03, 28.89it/s]Frame 100 done, time: 16.63733ms
119it [00:04, 30.01it/s]video (120/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
Frame 120 done, time: 16.59703ms
139it [00:04, 30.21it/s]Frame 140 done, time: 16.68978ms
147it [00:05, 29.64it/s]video (150/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
157it [00:05, 29.51it/s]Frame 160 done, time: 16.66093ms
180it [00:06, 28.88it/s]video (180/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
Frame 180 done, time: 16.58654ms
198it [00:07, 25.67it/s]Frame 200 done, time: 16.58106ms
210it [00:07, 26.60it/s]video (210/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
219it [00:07, 27.08it/s]Frame 220 done, time: 16.60180ms
240it [00:08, 27.42it/s]video (240/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
Frame 240 done, time: 16.58988ms
258it [00:09, 29.53it/s]Frame 260 done, time: 16.58177ms
269it [00:09, 29.89it/s]video (270/273) /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4:
273it [00:09, 28.13it/s]
Precision: 100.000%, mean cos sim: 0.990, num_TPs: 1474
Seq /mnt/diskb/even/dataset/MCMOT_Evaluate/val_15.mp4 done.

defaultdict(<class 'int'>, {0: 0, 5: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 1, 70: 1, 75: 11, 80: 29, 85: 97, 90: 630, 95: 13116})
defaultdict(<class 'int'>, {0: 0, 5: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 2, 75: 2, 80: 0, 85: 0, 90: 2, 95: 3})
Wrong [ 0, 5]: 0.000
Wrong [ 5, 10]: 0.000
Wrong [ 10, 15]: 0.000
Wrong [ 15, 20]: 0.000
Wrong [ 20, 25]: 0.000
Wrong [ 25, 30]: 0.000
Wrong [ 30, 35]: 0.000
Wrong [ 35, 40]: 0.000
Wrong [ 40, 45]: 0.000
Wrong [ 45, 50]: 0.000
Wrong [ 50, 55]: 0.000
Wrong [ 55, 60]: 0.000
Wrong [ 60, 65]: 0.000
Wrong [ 65, 70]: 0.000
Wrong [ 70, 75]: 0.014
Wrong [ 75, 80]: 0.014
Wrong [ 80, 85]: 0.000
Wrong [ 85, 90]: 0.000
Wrong [ 90, 95]: 0.014
Wrong [ 95, 100]: 0.022
Correct [ 0, 5]: 0.000
Correct [ 5, 10]: 0.000
Correct [ 10, 15]: 0.000
Correct [ 15, 20]: 0.000
Correct [ 20, 25]: 0.000
Correct [ 25, 30]: 0.000
Correct [ 30, 35]: 0.000
Correct [ 35, 40]: 0.000
Correct [ 40, 45]: 0.000
Correct [ 45, 50]: 0.000
Correct [ 50, 55]: 0.000
Correct [ 55, 60]: 0.000
Correct [ 60, 65]: 0.000
Correct [ 65, 70]: 0.007
Correct [ 70, 75]: 0.007
Correct [ 75, 80]: 0.079
Correct [ 80, 85]: 0.209
Correct [ 85, 90]: 0.698
Correct [ 90, 95]: 4.534
Correct [ 95, 100]: 94.400
Ratio [ 0, 5]: 0.000
Ratio [ 5, 10]: 0.000
Ratio [ 10, 15]: 0.005
Ratio [ 15, 20]: 0.015
Ratio [ 20, 25]: 0.055
Ratio [ 25, 30]: 0.123
Ratio [ 30, 35]: 0.689
Ratio [ 35, 40]: 1.281
Ratio [ 40, 45]: 2.186
Ratio [ 45, 50]: 3.994
Ratio [ 50, 55]: 7.053
Ratio [ 55, 60]: 9.223
Ratio [ 60, 65]: 10.898
Ratio [ 65, 70]: 10.639
Ratio [ 70, 75]: 10.749
Ratio [ 75, 80]: 10.558
Ratio [ 80, 85]: 10.573
Ratio [ 85, 90]: 6.510
Ratio [ 90, 95]: 2.992
Ratio [ 95, 100]: 12.457

Total 13997 true positives detected.
Total 13894 matches tested.
Num total match: 13894
Correct matched number: 13885
Wrong matched number: 9
Mean precision: 99.936%
Average precision: 99.935%
Min same ID similarity: 0.678
Max diff ID similarity: 0.976

Process finished with exit code 0