---
# System prepended metadata

title: AI_darknet Measuring performance
tags: [' darknet_test', handoverOK]

---

---
tags: handoverOK, darknet_test
---

# AI_darknet Measuring performance


### 5204 Enviorment
> 目前相關資源都放在 5204上~這台配有1T 的資料碟與256G ssd系統碟
> 你可存取5204 ~透過ssh scp SAMBA 
> SAMBA 和 SSH 帳密皆為aewin/aewin !
> 目前5204 IP為 192.168.99.90
```
ssh aewin@192.168.99.90
or 
scp aewin@192.168.99.90:/home/aewin/test_file ./your_pc
```


# darknet source code
```
/home/aewin/work/darknet
```



# Darknet/yolo3 neural network (customer provides)

```

yolov3-tiny_100000.weights

yolov3-tiny.cfg
```

# test picture  1280*720

```
213.jpg  1280*720
```

![](https://i.imgur.com/DgH4HnS.png)


- How many GFlops(gigaFLOPS:Floating-point) is required for using yolov3-tiny_100000.weights and yolov3-tiny.cfg to inference single picture (720P)?


```
5.494BFLOPS
```


# GPU Performance test for NVIDIA Tesla P100 PCIe 16 GB on YOLO3

![](https://i.imgur.com/7Y2qpbY.png)


https://www.techpowerup.com/gpu-specs/tesla-p100-pcie-16-gb.c2888


![](https://i.imgur.com/1EFMoEB.png)

```
5.494BFLOPS / 0.005357 sec = 1025.6 Gflpos

```

# CPU Performance test for Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz  on YOLO3

![](https://i.imgur.com/pTZlTs6.png)
```
5.494BFLOPS / 2.71 sec = 2.02 Gflpos

```



# CPU Performance test for Intel(R) Atom(TM) Processor E3930 @ 1.30GHz on YOLO3

![](https://i.imgur.com/pTZlTs6.png)
```
5.494BFLOPS / 4.049135 sec = 1.356 Gflpos

```


-----------------------------------


# Camera environment
#### 3D  30fps :
![](https://i.imgur.com/AcV7KXH.png)

#### 2D 
```
   576* 576* 5fps
```  
#### calculation 1:

```
900camera * 5fps = 4500


150camera * 30fps * 2 (3D) = 9000


9000 + 4500 =  13500  frames / sec


算力： 13500 * 5.494BFLOPS = 74169 Gflops = 74 Tflops
```



# How many GPU card to be needed if the system could process inferencing for 13500 frames per second based on YoLOV3 


==NVIDIA GeForce GTX 1080 Ti==

![](https://i.imgur.com/08KcxjE.png)  

```
74 Tflops / 11.3 = 6.5486
```

==NVIDIA GeForce GTX 980==

![](https://i.imgur.com/mbqnzM9.png)
```
74 Tflops / 4.9 = 15
```


==NVIDIA Tesla P100==

![](https://i.imgur.com/GNv73PM.png)

```
74 Tflops / 9.5 = 7.7
```




# How many 3D and 2D camera we can set up for FPGA of 1 TFLOPS computing power to do inference






#### 3D/2D computing power per frame :

```
1920*1080=2073600 , 12.35 Gflops  (3D)
1280*720=921600   , 5.494 Gflops  (3D)
576*576=331776    , 2.03  Gflops  (2D)
648*480=307200    , 1.83  Gflops  (3D depth)
```

### 3D video
5.494 Gflops*30 fps = 164.82 Gflops

### 3D Depth
1.83Gflops * 30 fps = 54.9 Gflops 

### 3D total (Video + Depth)
164.82 + 54.9 = 220 Gflops

### 2D video ,5fps

2.03  Gflops * 5 fps = 10 Gflops

### 2D Video, 25fps

2.03  Gflops * 25 fps = 50 Gflops


## so, How many 2D cameras could be set up when we have set 3D camera of 1 unit for 1 TFLOPS computing power FPGA ?
```

1000 Gflops - (1* 220 Gflops) = 780 Gflops

780 Gflops / 10 Gflops =  78 cameras for 2D 5fps
780 Gflops / 50 Gflops =  15 cameras for 2D 25fps
```
### 3D camera of 2 unit ....
```
1000 Gflops - (2* 220 Gflops) = 560

560 Gflops / 10 Gflops =  56  cameras for 2D 5fps
560 Gflops / 50 Gflops =  11  cameras for 2D 25fps
```


### 3D camera of 3 unit ...
```
1000 Gflops - (3* 220 Gflops) = 340

340 Gflops/ 10 Gflops = 34 cameras for 2D 5fps
340 Gflops/ 50 Gflops = 6 cameras  for 2D 25fps
```

### 3D camera of 4 unit ...

```
1000 Gflops - (4* 220 Gflops) = 120

120 Gflops/ 10 Gflops = 12 cameras for 2D 5fps
120 Gflops/ 50 Gflops =  2 cameras  for 2D 25fps

```



PS: https://groups.google.com/forum/#!topic/darknet/W2jKc3ggUfg


