训练结果记录
train_dataset:day&night
| Time |
step |
learning_rate |
Loss |
mAP_car |
mAP_car_night |
mAP_person |
| 2017.12.4 |
100000 |
0.008 |
- |
0.5790 |
0.5478 |
0.1130 |
| 2017.12.4 |
200000 |
0.008 |
- |
0.6044 |
0.6196 |
0.1382 |
| 2017.12.4 |
300000 |
0.008 |
- |
0.5938 |
0.6191 |
0.1329 |
| 2017.12.4 |
330000 |
0.008 |
- |
0.6056 |
0.6376 |
0.1329 |
| 2017.12.4 |
350000 |
0.008 |
- |
0.6040 |
0.6529 |
0.1599 |
| 2017.12.4 |
370000 |
0.008 |
- |
0.5975 |
0.6197 |
0.1043 |
| 2017.12.4 |
400000 |
0.008 |
- |
0.6061 |
0.6097 |
0.1017 |
valid_dataset:day&night
| Time |
step |
learning_rate |
Loss |
mAP_car |
mAP_car_night |
mAP_person |
| 2017.12.4 |
100000 |
0.008 |
- |
0.6376 |
0.5769 |
0.1378 |
| 2017.12.4 |
200000 |
0.008 |
- |
0.6712 |
0.5662 |
0.1814 |
| 2017.12.4 |
300000 |
0.008 |
- |
0.6708 |
0.5918 |
0.2132 |
| 2017.12.4 |
330000 |
0.008 |
- |
0.6679 |
0.6268 |
0.1661 |
| 2017.12.4 |
350000 |
0.008 |
- |
0.6558 |
0.6369 |
0.1728 |
| 2017.12.4 |
370000 |
0.008 |
- |
0.6684 |
0.6069 |
0.2190 |
| 2017.12.4 |
400000 |
0.008 |
- |
0.6511 |
0.5870 |
0.1977 |
valid_dataset:night
| Time |
step |
learning_rate |
mAP_car_night |
| 2017.12.4 |
100000 |
0.008 |
0.5770 |
| 2017.12.4 |
200000 |
0.008 |
0.5662 |
| 2017.12.4 |
300000 |
0.008 |
0.5918 |
| 2017.12.4 |
330000 |
0.008 |
0.6307 |
| 2017.12.4 |
350000 |
0.008 |
0.6369 |
| 2017.12.4 |
370000 |
0.008 |
0.6069 |
| 2017.12.4 |
400000 |
0.008 |
0.5893 |
valid_dataset:day
| Time |
step |
learning_rate |
mAP_car |
mAP_person |
| 2017.12.4 |
100000 |
0.008 |
0.6403 |
0.1613 |
| 2017.12.4 |
200000 |
0.008 |
0.6796 |
0.2073 |
| 2017.12.4 |
300000 |
0.008 |
0.6764 |
0.2510 |
| 2017.12.4 |
330000 |
0.008 |
0.6696 |
0.2009 |
| 2017.12.4 |
350000 |
0.008 |
0.6604 |
0.2039 |
| 2017.12.4 |
370000 |
0.008 |
0.6767 |
0.2545 |
| 2017.12.4 |
400000 |
0.008 |
0.6596 |
0.2330 |
imagenet_anngic5:基于Imagenet160万次继续训练anngic5类
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
car_night |
elec |
bicycle |
| 2017.12.11 |
100000 |
0.008 |
- |
0.0623 |
0.4971 |
0.5874 |
0.2098 |
0.0531 |
| 2017.12.11 |
120000 |
0.008 |
- |
0.0761 |
0.5172 |
0.5946 |
0.2789 |
0.0810 |
| 2017.12.11 |
150000 |
0.008 |
- |
0.0950 |
0.5325 |
0.5893 |
0.2707 |
0.0873 |
| 2017.12.11 |
180000 |
0.008 |
- |
0.0853 |
0.5076 |
0.5960 |
0.2633 |
0.0589 |
| 2017.12.11 |
200000 |
0.008 |
- |
0.0799 |
0.5191 |
0.6133 |
0.2639 |
0.0509 |
| 2017.12.11 |
220000 |
0.008 |
- |
0.0975 |
0.5239 |
0.6120 |
0.2848 |
0.0797 |
| 2017.12.11 |
250000 |
0.008 |
- |
0.0833 |
0.5225 |
0.6166 |
0.2716 |
0.1077 |
| 2017.12.11 |
280000 |
0.008 |
- |
0.0960 |
0.5207 |
0.6263 |
0.2311 |
0.0729 |
| 2017.12.11 |
300000 |
0.008 |
- |
0.1073 |
0.5191 |
0.5955 |
0.2821 |
0.0433 |
| 2017.12.11 |
320000 |
0.008 |
- |
0.0890 |
0.5424 |
0.6204 |
0.2791 |
0.0817 |
imagenet_coco_anngic5:基于Imagenet160万次继续训练coco4类
写入面积>0.008的person的box；面积>0.0025的其他类的box；
从coco随机挑选5000张验证集，验证集未过滤小目标
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.11 |
120000 |
0.008 |
- |
0.2434 |
0.1266 |
0.2656 |
0.0830 |
| 2017.12.11 |
150000 |
0.008 |
- |
0.2523 |
0.1302 |
0.2638 |
0.1054 |
| 2017.12.11 |
200000 |
0.008 |
- |
0.2551 |
0.1361 |
0.2709 |
0.1141 |
| 2017.12.11 |
230000 |
0.008 |
- |
0.2602 |
0.1288 |
0.2785 |
0.1186 |
| 2017.12.11 |
250000 |
0.008 |
- |
0.2591 |
0.1351 |
0.1073 |
0.1567 |
若使用23800张验证集：
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.12 |
250000 |
0.008 |
- |
0.2173 |
0.1153 |
0.2316 |
0.0793 |
| 2017.12.12 |
300000 |
0.008 |
- |
0.2227 |
0.1212 |
0.2369 |
0.0876 |
imagenet_tiny_coco_anngic5:基于Imagenet160万次继续训练tiny-coco4类
写入面积>0.1的person的box；面积>0.0025的其他类的box；
从coco随机挑选5000张验证集，验证集未过滤小目标
删减人样本数量，提高样本均衡性
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.11 |
120000 |
0.008 |
- |
0.1492 |
0.1439 |
0.2949 |
0.1072 |
| 2017.12.11 |
150000 |
0.008 |
- |
0.1437 |
0.1548 |
0.2848 |
0.1087 |
| 2017.12.11 |
200000 |
0.008 |
- |
0.1579 |
0.1552 |
0.2847 |
0.1244 |
| 2017.12.11 |
230000 |
0.008 |
- |
0.1468 |
0.1483 |
0.2927 |
0.1158 |
| 2017.12.11 |
250000 |
0.008 |
- |
0.1669 |
0.1490 |
0.2952 |
0.1347 |
若使用23800张验证集：
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.12 |
250000 |
0.008 |
- |
0.1405 |
0.1244 |
0.2363 |
0.0961 |
| 2017.12.12 |
300000 |
0.008 |
- |
0.1157 |
0.1330 |
0.2341 |
0.0933 |
官网yolo.weights测试coco80类完整验证集（只展示需要类别）
| Time |
mAP_person |
car |
bus |
truck |
motorcycle |
bicycle |
| 2017.12.11 |
0.5872 |
0.2653 |
0.6051 |
0.3337 |
0.4762 |
0.2913 |
imagenet_coco5:基于Imagenet160万次继续训练coco4类
训练集：写入面积>0.008的person的box；面积>0.0025的其他类的box；
验证集:写入面积>0.008的person的box；面积>0.0025的其他类的box；
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.008 |
- |
0.4076 |
0.1776 |
0.2616 |
0.0988 |
| 2017.12.14 |
150000 |
0.008 |
- |
0.4237 |
0.1793 |
0.2442 |
0.1031 |
| 2017.12.14 |
200000 |
0.008 |
- |
0.4216 |
0.1794 |
0.2748 |
0.1124 |
继续基于imagenet_coco5_100000.weights 使用poly学习率训练
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.008000 |
9.495427 |
0.4076 |
0.1776 |
0.2616 |
0.0988 |
| 2017.12.15 |
150000 |
0.001221 |
9.663104 |
0.4460 |
0.2031 |
0.2969 |
0.1167 |
| 2017.12.15 |
170000 |
0.000875 |
8.008812 |
0.4547 |
0.2070 |
0.3045 |
0.1318 |
| 2017.12.15 |
200000 |
0.000500 |
8.623980 |
0.4762 |
0.2296 |
0.3138 |
0.1430 |
imagenet_tiny_coco5:基于Imagenet160万次继续训练coco4类
训练集：写入面积>0.008的person的box；面积>0.0025的其他类的box；
若一张图片中有10个以上面积<0.008的人 ，则删去这张图
验证集:写入面积>0.008的person的box；面积>0.0025的其他类的box；
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.008 |
- |
0.3995 |
0.1780 |
0.2453 |
0.1036 |
| 2017.12.14 |
150000 |
0.008 |
- |
0.4344 |
0.1849 |
0.2904 |
0.1177 |
| 2017.12.14 |
200000 |
0.008 |
- |
0.4162 |
0.1702 |
0.2713 |
0.1073 |
继续基于imagenet_tiny_coco5_100000.weights 使用poly学习率训练
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.00800 |
10.087712 |
0.3995 |
0.1780 |
0.2453 |
0.1036 |
| 2017.12.15 |
150000 |
0.001221 |
7.373747 |
0.4423 |
0.1749 |
0.2853 |
0.1285 |
| 2017.12.15 |
170000 |
0.000875 |
8.408542 |
0.4756 |
0.2216 |
0.3113 |
0.1420 |
| 2017.12.15 |
200000 |
0.000500 |
8.078330 |
0.4293 |
0.1714 |
0.2797 |
0.1176 |
imagenet_tiny_coco_2:基于Imagenet160万次继续训练coco4类
训练集：写入面积>0.008且<0.52的person的box；面积>0.0025的其他类的box；（与imagenet_tiny_coco_3区别：失误在每个labels里面重复写了一遍box，故2中labels大小是3的两倍）
验证集:写入面积>0.008的person的box；面积>0.0025的其他类的box；
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.008 |
- |
0.4141 |
0.1873 |
0.2737 |
0.1052 |
| 2017.12.14 |
130000 |
0.008 |
- |
0.4214 |
0.1732 |
0.2605 |
0.0996 |
| 2017.12.14 |
140000 |
0.008 |
- |
0.4165 |
0.1879 |
0.2692 |
0.1037 |
| 2017.12.14 |
150000 |
0.008 |
- |
0.3857 |
0.1652 |
0.2482 |
0.0981 |
继续基于imagenet_tiny_coco5_2_100000.weights 使用poly学习率训练
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.00800 |
10.520602 |
0.4141 |
0.1873 |
0.2737 |
0.1052 |
| 2017.12.15 |
150000 |
0.001221 |
8.319970 |
0.4019 |
0.1793 |
0.2709 |
0.1124 |
| 2017.12.15 |
170000 |
0.000875 |
8.257561 |
0.3695 |
0.1623 |
0.2619 |
0.1023 |
| 2017.12.15 |
200000 |
0.000500 |
9.154430 |
0.4716 |
0.2341 |
0.3270 |
0.1372 |
imagenet_tiny_coco_3:基于Imagenet160万次继续训练coco4类
训练集：写入面积>0.008且<0.52的person的box；面积>0.0025的其他类的box；
验证集:写入面积>0.008的person的box；面积>0.0025的其他类的box；
继续基于imagenet_tiny_coco5_3_100000.weights 使用poly学习率训练
| Time |
step |
learning_rate |
Loss |
mAP_person |
car |
elec |
bicycle |
| 2017.12.14 |
100000 |
0.008000 |
8.811008 |
0.3098 |
0.1217 |
0.1898 |
0.0747 |
| 2017.12.15 |
150000 |
0.001221 |
7.485109 |
0.4565 |
0.2063 |
0.2957 |
0.1306 |
| 2017.12.15 |
170000 |
0.000875 |
8.071655 |
0.4638 |
0.2264 |
0.3224 |
0.1418 |
| 2017.12.15 |
200000 |
0.000500 |
8.536077 |
0.4745 |
0.2373 |
0.3258 |
0.1490 |
基于imagenet_tiny_coco_3训练20万次基础，继续partial前13层训练anngic结果记录如下。
不partial 直接训练
