Object detection #4
iharsuvorau
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Ideas
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The first tutorial I tried for YOLO was https://pjreddie.com/darknet/yolo/. It's pretty straightforward. For this image:
it produced: $ ./darknet detect cfg/yolov3.cfg yolov3.weights /Users/ihar/ut/machine\ learning/ajapaik/postkaart-meelis-rammeld-teater-ugala.jpeg
layer filters size input output
0 conv 32 3 x 3 / 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BFLOPs
1 conv 64 3 x 3 / 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BFLOPs
2 conv 32 1 x 1 / 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BFLOPs
3 conv 64 3 x 3 / 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BFLOPs
4 res 1 304 x 304 x 64 -> 304 x 304 x 64
5 conv 128 3 x 3 / 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BFLOPs
6 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BFLOPs
7 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 3.407 BFLOPs
8 res 5 152 x 152 x 128 -> 152 x 152 x 128
9 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BFLOPs
10 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 3.407 BFLOPs
11 res 8 152 x 152 x 128 -> 152 x 152 x 128
12 conv 256 3 x 3 / 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BFLOPs
13 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
14 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
15 res 12 76 x 76 x 256 -> 76 x 76 x 256
16 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
17 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
18 res 15 76 x 76 x 256 -> 76 x 76 x 256
19 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
20 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
21 res 18 76 x 76 x 256 -> 76 x 76 x 256
22 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
23 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
24 res 21 76 x 76 x 256 -> 76 x 76 x 256
25 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
26 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
27 res 24 76 x 76 x 256 -> 76 x 76 x 256
28 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
29 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
30 res 27 76 x 76 x 256 -> 76 x 76 x 256
31 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
32 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
33 res 30 76 x 76 x 256 -> 76 x 76 x 256
34 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
35 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
36 res 33 76 x 76 x 256 -> 76 x 76 x 256
37 conv 512 3 x 3 / 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BFLOPs
38 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
39 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
40 res 37 38 x 38 x 512 -> 38 x 38 x 512
41 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
42 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
43 res 40 38 x 38 x 512 -> 38 x 38 x 512
44 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
45 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
46 res 43 38 x 38 x 512 -> 38 x 38 x 512
47 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
48 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
49 res 46 38 x 38 x 512 -> 38 x 38 x 512
50 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
51 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
52 res 49 38 x 38 x 512 -> 38 x 38 x 512
53 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
54 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
55 res 52 38 x 38 x 512 -> 38 x 38 x 512
56 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
57 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
58 res 55 38 x 38 x 512 -> 38 x 38 x 512
59 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
60 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
61 res 58 38 x 38 x 512 -> 38 x 38 x 512
62 conv 1024 3 x 3 / 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BFLOPs
63 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
64 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
65 res 62 19 x 19 x1024 -> 19 x 19 x1024
66 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
67 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
68 res 65 19 x 19 x1024 -> 19 x 19 x1024
69 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
70 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
71 res 68 19 x 19 x1024 -> 19 x 19 x1024
72 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
73 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
74 res 71 19 x 19 x1024 -> 19 x 19 x1024
75 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
76 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
77 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
78 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
79 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
80 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
81 conv 255 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BFLOPs
82 yolo
83 route 79
84 conv 256 1 x 1 / 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BFLOPs
85 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 38 x 38 x 768 -> 38 x 38 x 256 0.568 BFLOPs
88 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
89 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
90 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
91 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
92 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
93 conv 255 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BFLOPs
94 yolo
95 route 91
96 conv 128 1 x 1 / 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BFLOPs
97 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 76 x 76 x 384 -> 76 x 76 x 128 0.568 BFLOPs
100 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
101 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
102 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
103 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
104 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
105 conv 255 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BFLOPs
106 yolo
Loading weights from yolov3.weights...Done!
/Users/ihar/ut/machine learning/ajapaik/postkaart-meelis-rammeld-teater-ugala.jpeg: Predicted in 18.353725 seconds.
person: 99%
person: 94%
person: 72%
person: 100%and this:
It successfully detected 4 humans. However, we don't yet have the bounding boxes in a machine readable format yet. This was the first approach to YOLO. |
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The second approach was to try YOLO through a package to have more control. I've tried this one https://github.com/r3tex/ObjectDetector.jl on these guys:
Black boxes are detections. So, this approach gives us the bounding boxes' coordinates already. |
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Yea. I did something similar - trying out selected images from the Partner's datasource on the COCO dataset. Most of the objects of interest to the partner(from prior communication) were not contained in this data set.
So currently, testing on the 168GB ImageNet dataset which contains more objects of interest |
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The task for object detection is more straightforward than for image area detection:
The tools for object detection and for image area detection are the same (see the discussion on Image area detection).
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