# Problem that can not display image to be tested with Deep learning model YOLOv4

**URL:** <https://discourse.jupyter.org/t/problem-that-can-not-display-image-to-be-tested-with-deep-learning-model-yolov4/7245>\
**Category:** Notebook\
**Created:** [December 23, 2020, 12:21am UTC](https://discourse.jupyter.org/t/problem-that-can-not-display-image-to-be-tested-with-deep-learning-model-yolov4/7245 "2020-12-23T00:21:06Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![That](https://avatars.discourse-cdn.com/v4/letter/t/958977/32.png) [@That](https://discourse.jupyter.org/u/That)\
**Post date:** [December 23, 2020, 12:21am UTC](https://discourse.jupyter.org/t/problem-that-can-not-display-image-to-be-tested-with-deep-learning-model-yolov4/7245/1 "2020-12-23T00:21:06Z")

</div>

Hi

I have a problem that i can not display an image on a notebook when I tested with deep learning model YOLOv4

Herein the code and error

> !./darknet detector test data/ceng.data cfg/yolov4-custom.cfg backup/yolov4-custom\_5000.weights -dont\_show test.jpg
> 
> CUDA-version: 10010 (10010), cuDNN: 7.6.5, GPU count: 1  
> OpenCV version: 3.2.0  
> 0 : compute\_capability = 750, cudnn\_half = 0, GPU: Tesla T4  
> net.optimized\_memory = 0  
> mini\_batch = 1, batch = 64, time\_steps = 1, train = 0  
> layer filters size/strd(dil) input output  
> 0 conv 32 3 x 3/ 1 320 x 320 x 3 → 320 x 320 x 32 0.177 BF  
> 1 conv 64 3 x 3/ 2 320 x 320 x 32 → 160 x 160 x 64 0.944 BF  
> 2 conv 64 1 x 1/ 1 160 x 160 x 64 → 160 x 160 x 64 0.210 BF  
> 3 route 1 → 160 x 160 x 64  
> 4 conv 64 1 x 1/ 1 160 x 160 x 64 → 160 x 160 x 64 0.210 BF  
> 5 conv 32 1 x 1/ 1 160 x 160 x 64 → 160 x 160 x 32 0.105 BF  
> 6 conv 64 3 x 3/ 1 160 x 160 x 32 → 160 x 160 x 64 0.944 BF  
> 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 160 x 160 x 64 0.002 BF  
> 8 conv 64 1 x 1/ 1 160 x 160 x 64 → 160 x 160 x 64 0.210 BF  
> 9 route 8 2 → 160 x 160 x 128  
> 10 conv 64 1 x 1/ 1 160 x 160 x 128 → 160 x 160 x 64 0.419 BF  
> 11 conv 128 3 x 3/ 2 160 x 160 x 64 → 80 x 80 x 128 0.944 BF  
> 12 conv 64 1 x 1/ 1 80 x 80 x 128 → 80 x 80 x 64 0.105 BF  
> 13 route 11 → 80 x 80 x 128  
> 14 conv 64 1 x 1/ 1 80 x 80 x 128 → 80 x 80 x 64 0.105 BF  
> 15 conv 64 1 x 1/ 1 80 x 80 x 64 → 80 x 80 x 64 0.052 BF  
> 16 conv 64 3 x 3/ 1 80 x 80 x 64 → 80 x 80 x 64 0.472 BF  
> 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 80 x 80 x 64 0.000 BF  
> 18 conv 64 1 x 1/ 1 80 x 80 x 64 → 80 x 80 x 64 0.052 BF  
> 19 conv 64 3 x 3/ 1 80 x 80 x 64 → 80 x 80 x 64 0.472 BF  
> 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 80 x 80 x 64 0.000 BF  
> 21 conv 64 1 x 1/ 1 80 x 80 x 64 → 80 x 80 x 64 0.052 BF  
> 22 route 21 12 → 80 x 80 x 128  
> 23 conv 128 1 x 1/ 1 80 x 80 x 128 → 80 x 80 x 128 0.210 BF  
> 24 conv 256 3 x 3/ 2 80 x 80 x 128 → 40 x 40 x 256 0.944 BF  
> 25 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 26 route 24 → 40 x 40 x 256  
> 27 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 28 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 29 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 31 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 32 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 34 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 35 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 37 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 38 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 40 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 41 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 43 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 44 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 46 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 47 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 49 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 50 conv 128 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 128 0.472 BF  
> 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 40 x 40 x 128 0.000 BF  
> 52 conv 128 1 x 1/ 1 40 x 40 x 128 → 40 x 40 x 128 0.052 BF  
> 53 route 52 25 → 40 x 40 x 256  
> 54 conv 256 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 256 0.210 BF  
> 55 conv 512 3 x 3/ 2 40 x 40 x 256 → 20 x 20 x 512 0.944 BF  
> 56 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 57 route 55 → 20 x 20 x 512  
> 58 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 59 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 60 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 62 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 63 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 65 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 66 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 68 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 69 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 71 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 72 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 74 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 75 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 77 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 78 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 80 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 81 conv 256 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 256 0.472 BF  
> 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 20 x 20 x 256 0.000 BF  
> 83 conv 256 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 256 0.052 BF  
> 84 route 83 56 → 20 x 20 x 512  
> 85 conv 512 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 512 0.210 BF  
> 86 conv 1024 3 x 3/ 2 20 x 20 x 512 → 10 x 10 x1024 0.944 BF  
> 87 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 88 route 86 → 10 x 10 x1024  
> 89 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 90 conv 512 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 512 0.052 BF  
> 91 conv 512 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x 512 0.472 BF  
> 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 10 x 10 x 512 0.000 BF  
> 93 conv 512 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 512 0.052 BF  
> 94 conv 512 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x 512 0.472 BF  
> 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 10 x 10 x 512 0.000 BF  
> 96 conv 512 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 512 0.052 BF  
> 97 conv 512 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x 512 0.472 BF  
> 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 10 x 10 x 512 0.000 BF  
> 99 conv 512 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 512 0.052 BF  
> 100 conv 512 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x 512 0.472 BF  
> 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 10 x 10 x 512 0.000 BF  
> 102 conv 512 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 512 0.052 BF  
> 103 route 102 87 → 10 x 10 x1024  
> 104 conv 1024 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x1024 0.210 BF  
> 105 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 106 conv 1024 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x1024 0.944 BF  
> 107 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 108 max 5x 5/ 1 10 x 10 x 512 → 10 x 10 x 512 0.001 BF  
> 109 route 107 → 10 x 10 x 512  
> 110 max 9x 9/ 1 10 x 10 x 512 → 10 x 10 x 512 0.004 BF  
> 111 route 107 → 10 x 10 x 512  
> 112 max 13x13/ 1 10 x 10 x 512 → 10 x 10 x 512 0.009 BF  
> 113 route 112 110 108 107 → 10 x 10 x2048  
> 114 conv 512 1 x 1/ 1 10 x 10 x2048 → 10 x 10 x 512 0.210 BF  
> 115 conv 1024 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x1024 0.944 BF  
> 116 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 117 conv 256 1 x 1/ 1 10 x 10 x 512 → 10 x 10 x 256 0.026 BF  
> 118 upsample 2x 10 x 10 x 256 → 20 x 20 x 256  
> 119 route 85 → 20 x 20 x 512  
> 120 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 121 route 120 118 → 20 x 20 x 512  
> 122 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 123 conv 512 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 512 0.944 BF  
> 124 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 125 conv 512 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 512 0.944 BF  
> 126 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 127 conv 128 1 x 1/ 1 20 x 20 x 256 → 20 x 20 x 128 0.026 BF  
> 128 upsample 2x 20 x 20 x 128 → 40 x 40 x 128  
> 129 route 54 → 40 x 40 x 256  
> 130 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 131 route 130 128 → 40 x 40 x 256  
> 132 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 133 conv 256 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 256 0.944 BF  
> 134 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 135 conv 256 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 256 0.944 BF  
> 136 conv 128 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 128 0.105 BF  
> 137 conv 256 3 x 3/ 1 40 x 40 x 128 → 40 x 40 x 256 0.944 BF  
> 138 conv 30 1 x 1/ 1 40 x 40 x 256 → 40 x 40 x 30 0.025 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 → 40 x 40 x 128  
> 141 conv 256 3 x 3/ 2 40 x 40 x 128 → 20 x 20 x 256 0.236 BF  
> 142 route 141 126 → 20 x 20 x 512  
> 143 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 144 conv 512 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 512 0.944 BF  
> 145 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 146 conv 512 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 512 0.944 BF  
> 147 conv 256 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 256 0.105 BF  
> 148 conv 512 3 x 3/ 1 20 x 20 x 256 → 20 x 20 x 512 0.944 BF  
> 149 conv 30 1 x 1/ 1 20 x 20 x 512 → 20 x 20 x 30 0.012 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 → 20 x 20 x 256  
> 152 conv 512 3 x 3/ 2 20 x 20 x 256 → 10 x 10 x 512 0.236 BF  
> 153 route 152 116 → 10 x 10 x1024  
> 154 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 155 conv 1024 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x1024 0.944 BF  
> 156 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 157 conv 1024 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x1024 0.944 BF  
> 158 conv 512 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 512 0.105 BF  
> 159 conv 1024 3 x 3/ 1 10 x 10 x 512 → 10 x 10 x1024 0.944 BF  
> 160 conv 30 1 x 1/ 1 10 x 10 x1024 → 10 x 10 x 30 0.006 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 35.262  
> avg\_outputs = 290120  
> Allocate additional workspace\_size = 52.43 MB  
> Loading weights from backup/yolov4-custom\_5000.weights…  
> seen 64, trained: 320 K-images (5 Kilo-batches\_64)  
> Done! Loaded 162 layers from weights-file  
> Detection layer: 139 - type = 28  
> Detection layer: 150 - type = 28  
> Detection layer: 161 - type = 28  
> test.jpg: Predicted in 32.533000 milli-seconds.  
> car: 98%  
> car: 90%  
> car: 46%  
> car: 74%
> 
> import cv2
> 
> cv2\_imshow(test.jpg)
> 
> Error
> 
> * * *
> 
> AttributeError Traceback (most recent call last)
> 
> in () ----\> 1 cv2\_imshow(test.jpg)
> 
> AttributeError: ‘PytestTester’ object has no attribute ‘jpg’

---

<div class="post-metadata">

**Author:** ![fomightez](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/fomightez/32/495_2.png) [@fomightez](https://discourse.jupyter.org/u/fomightez)\
**Post date:** [December 23, 2020, 4:11pm UTC](https://discourse.jupyter.org/t/problem-that-can-not-display-image-to-be-tested-with-deep-learning-model-yolov4/7245/2 "2020-12-23T16:11:00Z")

</div>

This isn’t a Jupyter issue. Looks to be just basic Python syntax, although maybe in pasting things some of the context got lost? (Note, to avoid that possibility in the future when you ask questions in appropriate locations you could take the notebook, edited down to the few cells you want to share, and post it as a gist at [https://gist.github.com](https://gist.github.com) and share a link to it rendered via [https://nbviewer.jupyter.org/](https://nbviewer.jupyter.org/) along with the basics of your question.)  
Did you try putting quotes around the file name, i.e., `cv2_imshow("test.jpg")`? Plus, I take it you are on Google Colab and imported `cv2_imshow` correctly (see [here](http://dsbyprateekg.blogspot.com/2020/08/how-to-use-opencv-python-with-darknets.html))?