yolov7
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WongKinYiu
Small object under 16px detection
For yolov4, @AlexeyAB suggests to do the following modification in order to detect objects smaller 16px: " for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set layers = 23 instead of
[darknet/cfg/yolov4.cfg](https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895)
Line 895 in [6f718c2](https://github.com/AlexeyAB/darknet/commit/6f718c257815a984253346bba8fb7aa756c55090)
layers = 54
set stride=4 instead of
[darknet/cfg/yolov4.cfg](https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892)
Line 892 in [6f718c2](https://github.com/AlexeyAB/darknet/commit/6f718c257815a984253346bba8fb7aa756c55090)
stride=2
set stride=4 instead of
[darknet/cfg/yolov4.cfg](https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989)
Line 989 in [6f718c2](https://github.com/AlexeyAB/darknet/commit/6f718c257815a984253346bba8fb7aa756c55090)
stride=2
" Which make a significant improvement for my use-case (single class & small object ) .
Is there a way to do the same trick with yolov7 (and even yolov7-tiny )?
I train with : python3 train.py --weights yolov7_training.pt --data "custom.yaml" --workers 4 --batch-size 4 --img 1024 --cfg cfg/training/yolov7.yaml --name yolov7 --hyp data/hyp.scratch.p5.yaml --epochs 200
For now I don't achieve to get the same [email protected] than yolov4 and I use: Use auto anchors (3,3, 5,4, 7,5, 6,6, 8,7, 10,10, 13,12, 18,18, 27,37) Use Adam Set learning rate to 0.001 warmup at 0 Disable mosaic & mixup Set scale to 0.1
In advance thanks.
Try:
[-1, 1, nn.Upsample, [None, 4, 'nearest']],
https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L78
[24, 1, Conv, [256, 1, 1]],
https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L79
[11, 1, Conv, [128, 1, 1]],
https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L93
Thank you for your reply! I try this, but got an error:
Sizes of tensors must match except in dimension 1. Expected size 16 but got size 8 for tensor number 2 in the list. File "D:\Code\DLProject\pytorch-yolov7-WongKinYiu\models\common.py", line 64, in forward return torch.cat(x, dim=self.d) File "D:\Code\DLProject\pytorch-yolov7-WongKinYiu\models\yolo.py", line 613, in forward_once x = m(x) # run File "D:\Code\DLProject\pytorch-yolov7-WongKinYiu\models\yolo.py", line 587, in forward return self.forward_once(x, profile) # single-scale inference, train File "D:\Code\DLProject\pytorch-yolov7-WongKinYiu\models\yolo.py", line 532, in init m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward File "D:\Code\DLProject\pytorch-yolov7-WongKinYiu\train.py", line 88, in train
Try this cfg file: yolov7_so.zip
I used
[-1, 1, nn.Upsample, [None, 4, 'nearest']],
[11, 1, Conv, [128, 1, 1]], # route backbone P3
instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93
and
[-1, 1, Conv, [128, 3, 4]],
instead of
https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L108
Hi @loveq007,
I did the same for yolov7-tiny and I get better results:
[-1, 1, nn.Upsample, [None, 4, 'nearest']], # Upsample 4 instead of 2
[7, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 instead of 14
Instead of:
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L76 https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L77
and
[-1, 1, Conv, [128, 3, 4, None, 1, nn.LeakyReLU(0.1)]], # stride 2 to 4
instead of
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L87
Try this cfg file: yolov7_so.zip
I used
[-1, 1, nn.Upsample, [None, 4, 'nearest']], [11, 1, Conv, [128, 1, 1]], # route backbone P3instead of
https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93
and
[-1, 1, Conv, [128, 3, 4]],instead ofhttps://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L108
when I verify the yolov7_so.yaml, then get some errors:
python models/yolo.py --config ./cfg/training/yolov7_so.yaml
from n params module arguments
0 -1 1 928 models.common.Conv [3, 32, 3, 1]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 36992 models.common.Conv [64, 64, 3, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 8320 models.common.Conv [128, 64, 1, 1]
5 -2 1 8320 models.common.Conv [128, 64, 1, 1]
6 -1 1 36992 models.common.Conv [64, 64, 3, 1]
7 -1 1 36992 models.common.Conv [64, 64, 3, 1]
8 -1 1 36992 models.common.Conv [64, 64, 3, 1]
9 -1 1 36992 models.common.Conv [64, 64, 3, 1]
10 [-1, -3, -5, -6] 1 0 models.common.Concat [1]
11 -1 1 66048 models.common.Conv [256, 256, 1, 1]
12 -1 1 0 models.common.MP []
13 -1 1 33024 models.common.Conv [256, 128, 1, 1]
14 -3 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 147712 models.common.Conv [128, 128, 3, 2]
16 [-1, -3] 1 0 models.common.Concat [1]
17 -1 1 33024 models.common.Conv [256, 128, 1, 1]
18 -2 1 33024 models.common.Conv [256, 128, 1, 1]
19 -1 1 147712 models.common.Conv [128, 128, 3, 1]
20 -1 1 147712 models.common.Conv [128, 128, 3, 1]
21 -1 1 147712 models.common.Conv [128, 128, 3, 1]
22 -1 1 147712 models.common.Conv [128, 128, 3, 1]
23 [-1, -3, -5, -6] 1 0 models.common.Concat [1]
24 -1 1 263168 models.common.Conv [512, 512, 1, 1]
25 -1 1 0 models.common.MP []
26 -1 1 131584 models.common.Conv [512, 256, 1, 1]
27 -3 1 131584 models.common.Conv [512, 256, 1, 1]
28 -1 1 590336 models.common.Conv [256, 256, 3, 2]
29 [-1, -3] 1 0 models.common.Concat [1]
30 -1 1 131584 models.common.Conv [512, 256, 1, 1]
31 -2 1 131584 models.common.Conv [512, 256, 1, 1]
32 -1 1 590336 models.common.Conv [256, 256, 3, 1]
33 -1 1 590336 models.common.Conv [256, 256, 3, 1]
34 -1 1 590336 models.common.Conv [256, 256, 3, 1]
35 -1 1 590336 models.common.Conv [256, 256, 3, 1]
36 [-1, -3, -5, -6] 1 0 models.common.Concat [1]
37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1]
38 -1 1 0 models.common.MP []
39 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
40 -3 1 525312 models.common.Conv [1024, 512, 1, 1]
41 -1 1 2360320 models.common.Conv [512, 512, 3, 2]
42 [-1, -3] 1 0 models.common.Concat [1]
43 -1 1 262656 models.common.Conv [1024, 256, 1, 1]
44 -2 1 262656 models.common.Conv [1024, 256, 1, 1]
45 -1 1 590336 models.common.Conv [256, 256, 3, 1]
46 -1 1 590336 models.common.Conv [256, 256, 3, 1]
47 -1 1 590336 models.common.Conv [256, 256, 3, 1]
48 -1 1 590336 models.common.Conv [256, 256, 3, 1]
49 [-1, -3, -5, -6] 1 0 models.common.Concat [1]
50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1]
51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1]
52 -1 1 131584 models.common.Conv [512, 256, 1, 1]
53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
54 37 1 262656 models.common.Conv [1024, 256, 1, 1]
55 [-1, -2] 1 0 models.common.Concat [1]
56 -1 1 131584 models.common.Conv [512, 256, 1, 1]
57 -2 1 131584 models.common.Conv [512, 256, 1, 1]
58 -1 1 295168 models.common.Conv [256, 128, 3, 1]
59 -1 1 147712 models.common.Conv [128, 128, 3, 1]
60 -1 1 147712 models.common.Conv [128, 128, 3, 1]
61 -1 1 147712 models.common.Conv [128, 128, 3, 1]
62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1]
63 -1 1 262656 models.common.Conv [1024, 256, 1, 1]
64 -1 1 33024 models.common.Conv [256, 128, 1, 1]
65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 4, 'nearest']
66 11 1 33024 models.common.Conv [256, 128, 1, 1]
67 [-1, -2] 1 0 models.common.Concat [1]
68 -1 1 33024 models.common.Conv [256, 128, 1, 1]
69 -2 1 33024 models.common.Conv [256, 128, 1, 1]
70 -1 1 73856 models.common.Conv [128, 64, 3, 1]
71 -1 1 36992 models.common.Conv [64, 64, 3, 1]
72 -1 1 36992 models.common.Conv [64, 64, 3, 1]
73 -1 1 36992 models.common.Conv [64, 64, 3, 1]
74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1]
75 -1 1 65792 models.common.Conv [512, 128, 1, 1]
76 -1 1 0 models.common.MP []
77 -1 1 16640 models.common.Conv [128, 128, 1, 1]
78 -3 1 16640 models.common.Conv [128, 128, 1, 1]
79 -1 1 147712 models.common.Conv [128, 128, 3, 4]
80 [-1, -3, 63] 1 0 models.common.Concat [1]
81 -1 1 131584 models.common.Conv [512, 256, 1, 1]
82 -2 1 131584 models.common.Conv [512, 256, 1, 1]
83 -1 1 295168 models.common.Conv [256, 128, 3, 1]
84 -1 1 147712 models.common.Conv [128, 128, 3, 1]
85 -1 1 147712 models.common.Conv [128, 128, 3, 1]
86 -1 1 147712 models.common.Conv [128, 128, 3, 1]
87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1]
88 -1 1 262656 models.common.Conv [1024, 256, 1, 1]
89 -1 1 0 models.common.MP []
90 -1 1 66048 models.common.Conv [256, 256, 1, 1]
91 -3 1 66048 models.common.Conv [256, 256, 1, 1]
92 -1 1 590336 models.common.Conv [256, 256, 3, 2]
93 [-1, -3, 51] 1 0 models.common.Concat [1]
94 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
95 -2 1 525312 models.common.Conv [1024, 512, 1, 1]
96 -1 1 1180160 models.common.Conv [512, 256, 3, 1]
97 -1 1 590336 models.common.Conv [256, 256, 3, 1]
98 -1 1 590336 models.common.Conv [256, 256, 3, 1]
99 -1 1 590336 models.common.Conv [256, 256, 3, 1]
100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1]
101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1]
102 75 1 328704 models.common.RepConv [128, 256, 3, 1]
103 88 1 1312768 models.common.RepConv [256, 512, 3, 1]
104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1]
105 [102, 103, 104] 1 460282 IDetect [80, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]
Traceback (most recent call last):
File "/home/yao/project/detection/yolov7/models/yolo.py", line 827, in <module>
model = Model(opt.cfg).to(device)
File "/home/yao/project/detection/yolov7/models/yolo.py", line 544, in __init__
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
File "/home/yao/project/detection/yolov7/models/yolo.py", line 599, in forward
return self.forward_once(x, profile) # single-scale inference, train
File "/home/yao/project/detection/yolov7/models/yolo.py", line 625, in forward_once
x = m(x) # run
File "/home/yao/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/yao/project/detection/yolov7/models/common.py", line 62, in forward
return torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 16 but got size 32 for tensor number 1 in the list.
Hi @loveq007,
I did the same for yolov7-tiny and I get better results:
[-1, 1, nn.Upsample, [None, 4, 'nearest']], # Upsample 4 instead of 2 [7, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 instead of 14Instead of:
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L76
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L77
and
[-1, 1, Conv, [128, 3, 4, None, 1, nn.LeakyReLU(0.1)]], # stride 2 to 4instead of
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L87
Thank you! With this modification, I get better results too, but not better than yolov5s6.
I fixed and tested it. Try this cfg file: yolov7_so.zip
I used
[-1, 1, nn.Upsample, [None, 4, 'nearest']],
[11, 1, Conv, [128, 1, 1]], # route backbone P3
instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93
and
[-1, 1, Conv, [128, 1, 2]],
instead of
https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L106
and
[-1, 1, Conv, [128, 3, 4]],
instead of
https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L108
Thank you! With this modification, I get better results too, but not better than yolov5s6.
Or just try yolov7-w6
Hi @loveq007,
I did the same for yolov7-tiny and I get better results:
[-1, 1, nn.Upsample, [None, 4, 'nearest']], # Upsample 4 instead of 2 [7, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 instead of 14Instead of:
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L76
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L77
and
[-1, 1, Conv, [128, 3, 4, None, 1, nn.LeakyReLU(0.1)]], # stride 2 to 4instead of
https://github.com/WongKinYiu/yolov7/blob/892b603718cda9f118368a07dab700b5309c2ac5/cfg/training/yolov7-tiny.yaml#L87
May I ask, why did you make such a modification, and is there any basis for it?
@AlexeyAB
Hi I just need to detect vehicles and person . Video 4Mp when resized to yolov7 640 or 1280 small vehicles not detected.
What is the best way to detect smaller objects and keep the accuracy / speed ?
is "yolov7-w6" meant for this ?
there is a --img-size .... option for detect.py . Is it usefull to increase it to 2560 or so what is it affect ?
Best
I fixed and tested it. Try this cfg file: yolov7_so.zip
I used
[-1, 1, nn.Upsample, [None, 4, 'nearest']], [11, 1, Conv, [128, 1, 1]], # route backbone P3instead of
https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93
and
[-1, 1, Conv, [128, 1, 2]],instead ofhttps://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L106
and
[-1, 1, Conv, [128, 3, 4]],instead ofhttps://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L108
@AlexeyAB Hi Alexey, how do I implement this config for YOLOv7 Tiny?
I fixed and tested it. Try this cfg file: yolov7_so.zip I used
[-1, 1, nn.Upsample, [None, 4, 'nearest']], [11, 1, Conv, [128, 1, 1]], # route backbone P3instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93
and
[-1, 1, Conv, [128, 1, 2]],instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L106and
[-1, 1, Conv, [128, 3, 4]],instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L108@AlexeyAB Hi Alexey, how do I implement this config for YOLOv7 Tiny?
i use a same method for head model training but still its not giving proper bouning box
