https://github.com/sonhm3029/edabk_yolov4_weapondetection

This project using yolov4 for Weapon Detection problem
https://github.com/sonhm3029/edabk_yolov4_weapondetection

Last synced: 3 months ago
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This project using yolov4 for Weapon Detection problem

• Host: GitHub
• URL: https://github.com/sonhm3029/edabk_yolov4_weapondetection
• Owner: sonhm3029
• Created: 2022-10-11T01:58:11.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-10-14T06:13:22.000Z (over 2 years ago)
• Last Synced: 2025-01-17T01:25:16.568Z (4 months ago)
• Language: Jupyter Notebook
• Size: 1.18 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# EDABK_YOLOV4_WeaponDetection 

## I. Tham khảo

[Github](https://github.com/ari-dasci/OD-WeaponDetection/tree/master/Weapons%20and%20similar%20handled%20objects)

[Blog](https://miai.vn/2020/05/25/yolo-series-train-yolo-v4-train-tren-colab-chi-tiet-va-day-du-a-z/)

## II. Setup hyperparameters

### 1. yolov4 config

Sử dụng file custom config: [yolov4-custom.cfg](./yolov4-custom.cfg)

- [yolo.names](./yolo.names)

- [yolo.data](./yolo.data)

Các tham số lưu ý:

- subdivisions: 32

- width: 608

- height: 608

- classes:6

- max_batches = max(num_classes*2000, 6000) = 12000

- steps=9600,10800 (bằng 0.8, 0.9 max_batches)

- filters=33 ( dòng 963, 1051, 1139). Tính bằng (num_classes+5)*3

### 2. Makefile

- GPU=1

- CUDNN=1

- OPENCV=1

## III. Achitecture

```shell

conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF

   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF

   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF

   3 route  1 		                           ->  304 x 304 x  64 

   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF

   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF

   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF

   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 304 x 304 x  64 0.006 BF

   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF

   9 route  8 2 	                           ->  304 x 304 x 128 

  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF

  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF

  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF

  13 route  11 		                           ->  152 x 152 x 128 

  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF

  15 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF

  16 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF

  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF

  18 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF

  19 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF

  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 152 x 152 x  64 0.001 BF

  21 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF

  22 route  21 12 	                           ->  152 x 152 x 128 

  23 conv    128       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x 128 0.757 BF

  24 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF

  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

  26 route  24 		                           ->   76 x  76 x 256 

  27 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

  28 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  29 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  31 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  32 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  34 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  35 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  37 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  38 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  40 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  41 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  43 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  44 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  46 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  47 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  49 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  50 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF

  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  76 x  76 x 128 0.001 BF

  52 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF

  53 route  52 25 	                           ->   76 x  76 x 256 

  54 conv    256       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 256 0.757 BF

  55 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF

  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

  57 route  55 		                           ->   38 x  38 x 512 

  58 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

  59 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  60 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  62 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  63 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  65 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  66 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  68 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  69 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  71 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  72 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  74 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  75 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  77 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  78 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  80 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  81 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF

  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  38 x  38 x 256 0.000 BF

  83 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF

  84 route  83 56 	                           ->   38 x  38 x 512 

  85 conv    512       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 512 0.757 BF

  86 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF

  87 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

  88 route  86 		                           ->   19 x  19 x1024 

  89 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

  90 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF

  91 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF

  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF

  93 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF

  94 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF

  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF

  96 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF

  97 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF

  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF

  99 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF

 100 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF

 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  19 x  19 x 512 0.000 BF

 102 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF

 103 route  102 87 	                           ->   19 x  19 x1024 

 104 conv   1024       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x1024 0.757 BF

 105 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 106 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF

 107 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 108 max                5x 5/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.005 BF

 109 route  107 		                           ->   19 x  19 x 512 

 110 max                9x 9/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.015 BF

 111 route  107 		                           ->   19 x  19 x 512 

 112 max               13x13/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.031 BF

 113 route  112 110 108 107 	                   ->   19 x  19 x2048 

 114 conv    512       1 x 1/ 1     19 x  19 x2048 ->   19 x  19 x 512 0.757 BF

 115 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF

 116 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 117 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF

 118 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256

 119 route  85 		                           ->   38 x  38 x 512 

 120 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 121 route  120 118 	                           ->   38 x  38 x 512 

 122 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 123 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF

 124 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 125 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF

 126 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 127 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF

 128 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128

 129 route  54 		                           ->   76 x  76 x 256 

 130 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

 131 route  130 128 	                           ->   76 x  76 x 256 

 132 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

 133 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF

 134 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

 135 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF

 136 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF

 137 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF

 138 conv     33       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  33 0.098 BF

 139 yolo

[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20

nms_kind: greedynms (1), beta = 0.600000 

 140 route  136 		                           ->   76 x  76 x 128 

 141 conv    256       3 x 3/ 2     76 x  76 x 128 ->   38 x  38 x 256 0.852 BF

 142 route  141 126 	                           ->   38 x  38 x 512 

 143 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 144 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF

 145 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 146 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF

 147 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF

 148 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF

 149 conv     33       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  33 0.049 BF

 150 yolo

[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10

nms_kind: greedynms (1), beta = 0.600000 

 151 route  147 		                           ->   38 x  38 x 256 

 152 conv    512       3 x 3/ 2     38 x  38 x 256 ->   19 x  19 x 512 0.852 BF

 153 route  152 116 	                           ->   19 x  19 x1024 

 154 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 155 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF

 156 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 157 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF

 158 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF

 159 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF

 160 conv     33       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  33 0.024 BF

 161 yolo

[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05

nms_kind: greedynms (1), beta = 0.600000 

Total BFLOPS 127.310 

avg_outputs = 1047617 

```

## IV. Kết quả chạy

### 1. File weights

Dưới 1000 vòng lặp => save weights mỗi 100 vòng

Trên 1000 vòng lặp => save weights mỗi 10,000 vòng

[yolov4-custom_last.weights](https://drive.google.com/drive/folders/1j8IuxE9mM1FJqEwpBOwejllq90ilzqis?usp=sharing)

### 2. File logs

[yolov4.log](https://drive.google.com/file/d/1jFuoDcPgVR3Xx9ywdRtT26RAIka0Lw97/view?usp=sharing)

### 3. Loss

![train loss](./training_loss_plot.png)



_current avg loss = 1.5705_

_iteration = 1400_



### 4. Test với ảnh test

![](./images/test1.png)

![](./images/test2.png)

![](./images/test3.png)

![](./images/test4.png)