本文主要记录训练一类网络，修改网络参数，引起网络性能的变化
0.最原始的tiny-yolo
- 网络结构如下
[net]
#test
batch=1
subdivisions=1
#train
#batch=64
#subdivisions=4
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
max_batches = 40200
policy=steps
steps=-1,100,20000,30000
scales=.1,10,.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[region]
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=1
- Evaluate Recall
252 656 690 RPs/Img: 21.57 IOU: 75.29% Recall:95.07%
253 658 692 RPs/Img: 21.50 IOU: 75.30% Recall:95.09%
254 660 694 RPs/Img: 21.50 IOU: 75.33% Recall:95.10%
255 663 697 RPs/Img: 21.43 IOU: 75.40% Recall:95.12%
256 663 697 RPs/Img: 21.41 IOU: 75.40% Recall:95.12%
257 663 697 RPs/Img: 21.38 IOU: 75.40% Recall:95.12%
258 665 699 RPs/Img: 21.36 IOU: 75.41% Recall:95.14%
259 667 701 RPs/Img: 21.35 IOU: 75.40% Recall:95.15%
260 669 703 RPs/Img: 21.33 IOU: 75.42% Recall:95.16%
261 671 705 RPs/Img: 21.31 IOU: 75.42% Recall:95.18%
262 673 707 RPs/Img: 21.33 IOU: 75.43% Recall:95.19%
263 673 707 RPs/Img: 21.32 IOU: 75.43% Recall:95.19%
264 673 707 RPs/Img: 21.35 IOU: 75.43% Recall:95.19%
265 675 709 RPs/Img: 21.31 IOU: 75.43% Recall:95.20%
266 676 710 RPs/Img: 21.27 IOU: 75.41% Recall:95.21%
267 678 712 RPs/Img: 21.25 IOU: 75.41% Recall:95.22%
268 682 716 RPs/Img: 21.25 IOU: 75.44% Recall:95.25%
269 683 717 RPs/Img: 21.22 IOU: 75.44% Recall:95.26%
270 685 719 RPs/Img: 21.25 IOU: 75.46% Recall:95.27%
271 687 721 RPs/Img: 21.22 IOU: 75.48% Recall:95.28%
272 689 723 RPs/Img: 21.21 IOU: 75.47% Recall:95.30%
273 689 723 RPs/Img: 21.25 IOU: 75.47% Recall:95.30%
274 691 725 RPs/Img: 21.24 IOU: 75.50% Recall:95.31%
275 691 725 RPs/Img: 21.32 IOU: 75.50% Recall:95.31%
276 691 725 RPs/Img: 21.36 IOU: 75.50% Recall:95.31%
277 692 726 RPs/Img: 21.35 IOU: 75.52% Recall:95.32%
278 694 728 RPs/Img: 21.37 IOU: 75.50% Recall:95.33%
279 696 730 RPs/Img: 21.37 IOU: 75.51% Recall:95.34%
280 699 734 RPs/Img: 21.37 IOU: 75.46% Recall:95.23%
281 702 738 RPs/Img: 21.39 IOU: 75.37% Recall:95.12%
282 704 740 RPs/Img: 21.41 IOU: 75.37% Recall:95.14%
283 706 742 RPs/Img: 21.41 IOU: 75.41% Recall:95.15%
284 706 742 RPs/Img: 21.40 IOU: 75.41% Recall:95.15%
285 708 744 RPs/Img: 21.37 IOU: 75.41% Recall:95.16%
286 708 744 RPs/Img: 21.38 IOU: 75.41% Recall:95.16%
287 708 744 RPs/Img: 21.34 IOU: 75.41% Recall:95.16%
288 708 744 RPs/Img: 21.33 IOU: 75.41% Recall:95.16%
289 710 746 RPs/Img: 21.33 IOU: 75.44% Recall:95.17%
290 711 748 RPs/Img: 21.34 IOU: 75.38% Recall:95.05%
291 713 750 RPs/Img: 21.32 IOU: 75.41% Recall:95.07%
292 715 752 RPs/Img: 21.37 IOU: 75.37% Recall:95.08%
293 717 754 RPs/Img: 21.33 IOU: 75.40% Recall:95.09%
294 719 756 RPs/Img: 21.32 IOU: 75.42% Recall:95.11%
295 721 758 RPs/Img: 21.32 IOU: 75.41% Recall:95.12%
296 722 759 RPs/Img: 21.28 IOU: 75.42% Recall:95.13%
297 722 759 RPs/Img: 21.36 IOU: 75.42% Recall:95.13%
298 722 759 RPs/Img: 21.42 IOU: 75.42% Recall:95.13%
299 724 761 RPs/Img: 21.41 IOU: 75.44% Recall:95.14%
300 725 762 RPs/Img: 21.43 IOU: 75.44% Recall:95.14%
301 727 764 RPs/Img: 21.42 IOU: 75.48% Recall:95.16%
302 728 765 RPs/Img: 21.43 IOU: 75.46% Recall:95.16%
303 728 765 RPs/Img: 21.40 IOU: 75.46% Recall:95.16%
304 730 767 RPs/Img: 21.40 IOU: 75.48% Recall:95.18%
305 734 771 RPs/Img: 21.42 IOU: 75.50% Recall:95.20%
306 746 783 RPs/Img: 21.45 IOU: 75.59% Recall:95.27%
307 748 785 RPs/Img: 21.43 IOU: 75.62% Recall:95.29%
308 750 787 RPs/Img: 21.42 IOU: 75.59% Recall:95.30%
309 752 789 RPs/Img: 21.43 IOU: 75.62% Recall:95.31%
310 754 791 RPs/Img: 21.44 IOU: 75.63% Recall:95.32%
1.第一次修改
- 网络结构如下
[net]
#test
batch=1
subdivisions=1
#train
#batch=64
#subdivisions=4
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
max_batches = 40200
policy=steps
steps=-1,100,20000,30000
scales=.1,10,.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[region]
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=1
- Loss-iter Curve如下
image.png
- Evaluate Recall
252 604 690 RPs/Img: 92.45 IOU: 66.21% Recall:87.54%
253 605 692 RPs/Img: 92.43 IOU: 66.18% Recall:87.43%
254 607 694 RPs/Img: 92.44 IOU: 66.17% Recall:87.46%
255 610 697 RPs/Img: 92.37 IOU: 66.22% Recall:87.52%
256 610 697 RPs/Img: 92.28 IOU: 66.22% Recall:87.52%
257 610 697 RPs/Img: 92.21 IOU: 66.22% Recall:87.52%
258 611 699 RPs/Img: 92.13 IOU: 66.21% Recall:87.41%
259 612 701 RPs/Img: 92.07 IOU: 66.16% Recall:87.30%
260 614 703 RPs/Img: 91.99 IOU: 66.22% Recall:87.34%
261 616 705 RPs/Img: 92.05 IOU: 66.23% Recall:87.38%
262 618 707 RPs/Img: 92.05 IOU: 66.24% Recall:87.41%
263 618 707 RPs/Img: 92.20 IOU: 66.24% Recall:87.41%
264 618 707 RPs/Img: 92.20 IOU: 66.24% Recall:87.41%
265 620 709 RPs/Img: 92.12 IOU: 66.23% Recall:87.45%
266 621 710 RPs/Img: 92.27 IOU: 66.22% Recall:87.46%
267 623 712 RPs/Img: 92.22 IOU: 66.27% Recall:87.50%
268 624 716 RPs/Img: 92.25 IOU: 66.21% Recall:87.15%
269 625 717 RPs/Img: 92.29 IOU: 66.22% Recall:87.17%
270 627 719 RPs/Img: 92.26 IOU: 66.24% Recall:87.20%
271 629 721 RPs/Img: 92.26 IOU: 66.25% Recall:87.24%
272 631 723 RPs/Img: 92.18 IOU: 66.27% Recall:87.28%
273 631 723 RPs/Img: 92.32 IOU: 66.27% Recall:87.28%
274 633 725 RPs/Img: 92.29 IOU: 66.26% Recall:87.31%
275 633 725 RPs/Img: 92.35 IOU: 66.26% Recall:87.31%
276 633 725 RPs/Img: 92.28 IOU: 66.26% Recall:87.31%
277 634 726 RPs/Img: 92.26 IOU: 66.26% Recall:87.33%
278 636 728 RPs/Img: 92.18 IOU: 66.29% Recall:87.36%
279 637 730 RPs/Img: 92.23 IOU: 66.25% Recall:87.26%
280 640 734 RPs/Img: 92.23 IOU: 66.22% Recall:87.19%
281 642 738 RPs/Img: 92.19 IOU: 66.16% Recall:86.99%
282 644 740 RPs/Img: 92.32 IOU: 66.20% Recall:87.03%
283 646 742 RPs/Img: 92.23 IOU: 66.23% Recall:87.06%
284 646 742 RPs/Img: 92.24 IOU: 66.23% Recall:87.06%
285 648 744 RPs/Img: 92.09 IOU: 66.24% Recall:87.10%
286 648 744 RPs/Img: 92.00 IOU: 66.24% Recall:87.10%
287 648 744 RPs/Img: 92.06 IOU: 66.24% Recall:87.10%
288 648 744 RPs/Img: 92.02 IOU: 66.24% Recall:87.10%
289 650 746 RPs/Img: 92.13 IOU: 66.23% Recall:87.13%
290 650 748 RPs/Img: 92.23 IOU: 66.17% Recall:86.90%
291 652 750 RPs/Img: 92.15 IOU: 66.22% Recall:86.93%
292 652 752 RPs/Img: 92.14 IOU: 66.17% Recall:86.70%
293 654 754 RPs/Img: 92.12 IOU: 66.22% Recall:86.74%
294 656 756 RPs/Img: 92.14 IOU: 66.23% Recall:86.77%
295 657 758 RPs/Img: 92.16 IOU: 66.21% Recall:86.68%
296 658 759 RPs/Img: 92.16 IOU: 66.20% Recall:86.69%
297 658 759 RPs/Img: 92.36 IOU: 66.20% Recall:86.69%
298 658 759 RPs/Img: 92.43 IOU: 66.20% Recall:86.69%
299 660 761 RPs/Img: 92.35 IOU: 66.24% Recall:86.73%
300 660 762 RPs/Img: 92.35 IOU: 66.21% Recall:86.61%
301 662 764 RPs/Img: 92.34 IOU: 66.24% Recall:86.65%
302 663 765 RPs/Img: 92.36 IOU: 66.24% Recall:86.67%
303 663 765 RPs/Img: 92.20 IOU: 66.24% Recall:86.67%
304 665 767 RPs/Img: 92.06 IOU: 66.29% Recall:86.70%
305 669 771 RPs/Img: 92.07 IOU: 66.34% Recall:86.77%
306 681 783 RPs/Img: 92.02 IOU: 66.32% Recall:86.97%
307 683 785 RPs/Img: 91.98 IOU: 66.32% Recall:87.01%
308 685 787 RPs/Img: 92.00 IOU: 66.33% Recall:87.04%
309 687 789 RPs/Img: 92.01 IOU: 66.35% Recall:87.07%
310 689 791 RPs/Img: 91.99 IOU: 66.38% Recall:87.10%
2.第二次修改
- 网络结构如下
[net]
#test
#batch=1
#subdivisions=1
#train
batch=64
subdivisions=4
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
max_batches = 40200
policy=steps
steps=-1,100,20000,30000
scales=.1,10,.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
###New layer add ###
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[region]
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=1
- Evaluate Recall
252 639 690 RPs/Img: 33.14 IOU: 73.85% Recall:92.61%
253 640 692 RPs/Img: 33.11 IOU: 73.83% Recall:92.49%
254 642 694 RPs/Img: 33.09 IOU: 73.86% Recall:92.51%
255 645 697 RPs/Img: 33.05 IOU: 73.93% Recall:92.54%
256 645 697 RPs/Img: 33.03 IOU: 73.93% Recall:92.54%
257 645 697 RPs/Img: 33.02 IOU: 73.93% Recall:92.54%
258 647 699 RPs/Img: 32.93 IOU: 73.96% Recall:92.56%
259 648 701 RPs/Img: 32.95 IOU: 73.93% Recall:92.44%
260 650 703 RPs/Img: 32.91 IOU: 73.95% Recall:92.46%
261 652 705 RPs/Img: 32.92 IOU: 73.97% Recall:92.48%
262 654 707 RPs/Img: 32.92 IOU: 73.98% Recall:92.50%
263 654 707 RPs/Img: 32.91 IOU: 73.98% Recall:92.50%
264 654 707 RPs/Img: 32.91 IOU: 73.98% Recall:92.50%
265 656 709 RPs/Img: 32.90 IOU: 73.98% Recall:92.52%
266 656 710 RPs/Img: 32.82 IOU: 73.94% Recall:92.39%
267 658 712 RPs/Img: 32.80 IOU: 73.94% Recall:92.42%
268 662 716 RPs/Img: 32.84 IOU: 73.98% Recall:92.46%
269 663 717 RPs/Img: 32.80 IOU: 73.99% Recall:92.47%
270 664 719 RPs/Img: 32.83 IOU: 73.95% Recall:92.35%
271 666 721 RPs/Img: 32.80 IOU: 73.97% Recall:92.37%
272 668 723 RPs/Img: 32.79 IOU: 73.97% Recall:92.39%
273 668 723 RPs/Img: 32.89 IOU: 73.97% Recall:92.39%
274 670 725 RPs/Img: 32.83 IOU: 74.00% Recall:92.41%
275 670 725 RPs/Img: 32.88 IOU: 74.00% Recall:92.41%
276 670 725 RPs/Img: 32.87 IOU: 74.00% Recall:92.41%
277 671 726 RPs/Img: 32.81 IOU: 74.00% Recall:92.42%
278 673 728 RPs/Img: 32.80 IOU: 74.03% Recall:92.45%
279 674 730 RPs/Img: 32.85 IOU: 74.00% Recall:92.33%
280 677 734 RPs/Img: 32.89 IOU: 73.93% Recall:92.23%
281 679 738 RPs/Img: 32.90 IOU: 73.87% Recall:92.01%
282 681 740 RPs/Img: 32.92 IOU: 73.90% Recall:92.03%
283 683 742 RPs/Img: 32.90 IOU: 73.92% Recall:92.05%
284 683 742 RPs/Img: 32.90 IOU: 73.92% Recall:92.05%
285 685 744 RPs/Img: 32.87 IOU: 73.94% Recall:92.07%
286 685 744 RPs/Img: 32.82 IOU: 73.94% Recall:92.07%
287 685 744 RPs/Img: 32.77 IOU: 73.94% Recall:92.07%
288 685 744 RPs/Img: 32.74 IOU: 73.94% Recall:92.07%
289 687 746 RPs/Img: 32.81 IOU: 73.91% Recall:92.09%
290 689 748 RPs/Img: 32.84 IOU: 73.89% Recall:92.11%
291 691 750 RPs/Img: 32.82 IOU: 73.91% Recall:92.13%
292 693 752 RPs/Img: 32.86 IOU: 73.86% Recall:92.15%
293 695 754 RPs/Img: 32.84 IOU: 73.87% Recall:92.18%
294 697 756 RPs/Img: 32.89 IOU: 73.87% Recall:92.20%
295 699 758 RPs/Img: 32.91 IOU: 73.86% Recall:92.22%
296 700 759 RPs/Img: 32.83 IOU: 73.86% Recall:92.23%
297 700 759 RPs/Img: 32.84 IOU: 73.86% Recall:92.23%
298 700 759 RPs/Img: 32.89 IOU: 73.86% Recall:92.23%
299 702 761 RPs/Img: 32.86 IOU: 73.90% Recall:92.25%
300 703 762 RPs/Img: 32.83 IOU: 73.90% Recall:92.26%
301 705 764 RPs/Img: 32.84 IOU: 73.93% Recall:92.28%
302 706 765 RPs/Img: 32.82 IOU: 73.92% Recall:92.29%
303 706 765 RPs/Img: 32.81 IOU: 73.92% Recall:92.29%
304 708 767 RPs/Img: 32.76 IOU: 73.94% Recall:92.31%
305 712 771 RPs/Img: 32.78 IOU: 73.98% Recall:92.35%
306 724 783 RPs/Img: 32.81 IOU: 74.08% Recall:92.46%
307 726 785 RPs/Img: 32.81 IOU: 74.11% Recall:92.48%
308 728 787 RPs/Img: 32.79 IOU: 74.11% Recall:92.50%
309 730 789 RPs/Img: 32.81 IOU: 74.13% Recall:92.52%
310 732 791 RPs/Img: 32.84 IOU: 74.17% Recall:92.54%