稀疏训练指标异常

[Epoch 0/20, Batch 13/2302] [Losses: x 0.204015, y 0.214396, w 4.140403, h 1.606703, conf 30.286903, cls 0.171045, total 36.623466, recall: 0.00000, precision: 0.00000] [Epoch 0/20, Batch 23/2302] [Losses: x 0.216009, y 0.182937, w 2.846014, h 1.343955, conf 28.463878, cls 0.171810, total 33.224602, recall: 0.02047, precision: 0.07926] [Epoch 0/20, Batch 39/2302] [Losses: x 0.239066, y 0.192233, w 2.735792, h 2.118299, conf 28.024119, cls 0.179756, total 33.489265, recall: 0.00544, precision: 0.05238] [Epoch 0/20, Batch 73/2302] [Losses: x 0.209569, y 0.173580, w 0.820454, h 0.410769, conf 20.885465, cls 0.170385, total 22.670223, recall: 0.02878, precision: 0.12886] [Epoch 0/20, Batch 83/2302] [Losses: x 0.253361, y 0.169483, w 1.047872, h 0.685030, conf 19.446521, cls 0.170343, total 21.772610, recall: 0.04167, precision: 0.17255] [Epoch 0/20, Batch 89/2302] [Losses: x 0.246635, y 0.184126, w 1.413699, h 1.299393, conf 15.728958, cls 0.174179, total 19.046989, recall: 0.03855, precision: 0.15952] [Epoch 0/20, Batch 102/2302] [Losses: x 0.257247, y 0.206709, w 0.842265, h 0.752688, conf 12.285303, cls 0.169251, total 14.513463, recall: 0.04132, precision: 0.06991] ...... [Epoch 19/20, Batch 2261/2302] [Losses: x 0.158596, y 0.126152, w 0.243440, h 0.186063, conf 0.602620, cls 0.164563, total 1.481433, recall: 0.53978, precision: 0.06947] [Epoch 19/20, Batch 2271/2302] [Losses: x 0.169528, y 0.110445, w 0.261716, h 0.266443, conf 0.320310, cls 0.159760, total 1.288202, recall: 0.50892, precision: 0.09175] [Epoch 19/20, Batch 2276/2302] [Losses: x 0.131687, y 0.120498, w 0.229741, h 0.150297, conf 0.556042, cls 0.165255, total 1.353521, recall: 0.56410, precision: 0.06603] [Epoch 19/20, Batch 2285/2302] [Losses: x 0.151971, y 0.106440, w 0.196245, h 0.137052, conf 0.336564, cls 0.161891, total 1.090163, recall: 0.57516, precision: 0.04400] 0~20%:0.742166,20~40%:0.979765,40~60%:0.985903,60~80%:0.990159,80~100%:2.850679

训练过程如下

[Epoch 0/20, Batch 13/2302] [Losses: x 0.204015, y 0.214396, w 4.140403, h 1.606703, conf 30.286903, cls 0.171045, total 36.623466, recall: 0.00000, precision: 0.00000]
[Epoch 0/20, Batch 23/2302] [Losses: x 0.216009, y 0.182937, w 2.846014, h 1.343955, conf 28.463878, cls 0.171810, total 33.224602, recall: 0.02047, precision: 0.07926]
[Epoch 0/20, Batch 39/2302] [Losses: x 0.239066, y 0.192233, w 2.735792, h 2.118299, conf 28.024119, cls 0.179756, total 33.489265, recall: 0.00544, precision: 0.05238]
[Epoch 0/20, Batch 73/2302] [Losses: x 0.209569, y 0.173580, w 0.820454, h 0.410769, conf 20.885465, cls 0.170385, total 22.670223, recall: 0.02878, precision: 0.12886]
[Epoch 0/20, Batch 83/2302] [Losses: x 0.253361, y 0.169483, w 1.047872, h 0.685030, conf 19.446521, cls 0.170343, total 21.772610, recall: 0.04167, precision: 0.17255]
[Epoch 0/20, Batch 89/2302] [Losses: x 0.246635, y 0.184126, w 1.413699, h 1.299393, conf 15.728958, cls 0.174179, total 19.046989, recall: 0.03855, precision: 0.15952]
[Epoch 0/20, Batch 102/2302] [Losses: x 0.257247, y 0.206709, w 0.842265, h 0.752688, conf 12.285303, cls 0.169251, total 14.513463, recall: 0.04132, precision: 0.06991]
......
[Epoch 19/20, Batch 2261/2302] [Losses: x 0.158596, y 0.126152, w 0.243440, h 0.186063, conf 0.602620, cls 0.164563, total 1.481433, recall: 0.53978, precision: 0.06947]
[Epoch 19/20, Batch 2271/2302] [Losses: x 0.169528, y 0.110445, w 0.261716, h 0.266443, conf 0.320310, cls 0.159760, total 1.288202, recall: 0.50892, precision: 0.09175]
[Epoch 19/20, Batch 2276/2302] [Losses: x 0.131687, y 0.120498, w 0.229741, h 0.150297, conf 0.556042, cls 0.165255, total 1.353521, recall: 0.56410, precision: 0.06603]
[Epoch 19/20, Batch 2285/2302] [Losses: x 0.151971, y 0.106440, w 0.196245, h 0.137052, conf 0.336564, cls 0.161891, total 1.090163, recall: 0.57516, precision: 0.04400]
0~20%:0.742166,20~40%:0.979765,40~60%:0.985903,60~80%:0.990159,80~100%:2.850679

看上不不正常，与#2 (comment) 相似，请问有人遇到过吗，有什么解决方法？

请问您找出问题了吗？我训练的时候最后一个稀疏指标也是这样的但是不知道这几个参数代表什么。

talebolano / yolov3-network-slimming

稀疏训练指标异常 #34