稀疏训练指标异常

[hemp110]

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？

[weixiaolian21]

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。