---
# System prepended metadata

title: Jetson AGX Xavier

---

# Jetson AGX Xavier

## 1. Installing Ubuntu 18.04 on Jetson AGX

Refs:
[youtube](https://www.youtube.com/watch?v=drBKVbOmPEk)
[jetson hack github](https://github.com/jetsonhacks/bootFromExternalStorage)

## 2. Installation Notes

- 32gb internal emmc disk is accidentally formatted without any backup, sorry for this huge blunder, 
but Ubuntu 18.04 can be booted up directly via ssd 500gb
- Jetpack is installed via apt
- opencv4 has been installed, even contrib is installed
- ros version is melodic, cv_bridge with 20.04 might hard to work with I guess
- git is set up to ical account, which is tested that can push project on ical gitlab
    - user.name: ical
    - user.email:nukical@gmail.com
    - ssh-key comment: ical_xavier_agx_sylvex (i have no idea how to name this so...)

## 3. Tasks

1. yolo testing
    
    - dataset

    http://140.127.205.183:5000/sharing/OE4Umn0kv
    cuda cudnn, gpu is configured true in darknet config
    .bashrc is configured for cuda and cudnn
    
    - training

    ![training](https://i.imgur.com/xc0zA6T.png)


    With Default Desktop Power Settings
    Pic_num=8, max_batch=1500, classes=3
    start from: 22:38 to the next day 00:14
    time_elapsed: 1hr36mins
    
    - recognising

    one of prediction -1.397 secs
    ![pic 4 prediction](https://i.imgur.com/YprHSP1.jpg)

    ```./darknet detector test ../yolo_mark/train.data ../yolo_mark/yolov3-tiny-train.cfg ../yolo_mark/shape/model/yolov3-tiny-train_last.weights ../yolo_mark/shape/img/4.JPG ```
    
    LOG:
    ```
     CUDA-version: 10020 (10020), cuDNN: 8.2.1, CUDNN_HALF=1, GPU count: 1  
     CUDNN_HALF=1 
     OpenCV version: 4.1.1
     0 : compute_capability = 720, cudnn_half = 1, GPU: Xavier 
    net.optimized_memory = 0 
    mini_batch = 1, batch = 32, time_steps = 1, train = 0 
       layer   filters  size/strd(dil)      input                output
       0 Create CUDA-stream - 0 
     Create cudnn-handle 0 
    conv     16       3 x 3/ 1    640 x 480 x   3 ->  640 x 480 x  16 0.265 BF
       1 max                2x 2/ 2    640 x 480 x  16 ->  320 x 240 x  16 0.005 BF
       2 conv     32       3 x 3/ 1    320 x 240 x  16 ->  320 x 240 x  32 0.708 BF
       3 max                2x 2/ 2    320 x 240 x  32 ->  160 x 120 x  32 0.002 BF
       4 conv     64       3 x 3/ 1    160 x 120 x  32 ->  160 x 120 x  64 0.708 BF
       5 max                2x 2/ 2    160 x 120 x  64 ->   80 x  60 x  64 0.001 BF
       6 conv    128       3 x 3/ 1     80 x  60 x  64 ->   80 x  60 x 128 0.708 BF
       7 max                2x 2/ 2     80 x  60 x 128 ->   40 x  30 x 128 0.001 BF
       8 conv    256       3 x 3/ 1     40 x  30 x 128 ->   40 x  30 x 256 0.708 BF
       9 max                2x 2/ 2     40 x  30 x 256 ->   20 x  15 x 256 0.000 BF
      10 conv    512       3 x 3/ 1     20 x  15 x 256 ->   20 x  15 x 512 0.708 BF
      11 max                2x 2/ 1     20 x  15 x 512 ->   20 x  15 x 512 0.001 BF
      12 conv   1024       3 x 3/ 1     20 x  15 x 512 ->   20 x  15 x1024 2.831 BF
      13 conv    256       1 x 1/ 1     20 x  15 x1024 ->   20 x  15 x 256 0.157 BF
      14 conv    512       3 x 3/ 1     20 x  15 x 256 ->   20 x  15 x 512 0.708 BF
      15 conv     24       1 x 1/ 1     20 x  15 x 512 ->   20 x  15 x  24 0.007 BF
      16 yolo
    [yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
      17 route  13 		                           ->   20 x  15 x 256 
      18 conv    128       1 x 1/ 1     20 x  15 x 256 ->   20 x  15 x 128 0.020 BF
      19 upsample                 2x    20 x  15 x 128 ->   40 x  30 x 128
      20 route  19 8 	                           ->   40 x  30 x 384 
      21 conv    256       3 x 3/ 1     40 x  30 x 384 ->   40 x  30 x 256 2.123 BF
      22 conv     24       1 x 1/ 1     40 x  30 x 256 ->   40 x  30 x  24 0.015 BF
      23 yolo
    [yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
    Total BFLOPS 9.676 
    avg_outputs = 577400 
     Allocate additional workspace_size = 52.44 MB 
    Loading weights from ../yolo_mark/shape/model/yolov3-tiny-train_last.weights...
     seen 64, trained: 48 K-images (0 Kilo-batches_64) 
    Done! Loaded 24 layers from weights-file 
     Detection layer: 16 - type = 28 
     Detection layer: 23 - type = 28 
    ../yolo_mark/shape/img/4.JPG: Predicted in 1397.593000 milli-seconds.
    ```
    
    benchmark
    
    video:
    ```./darknet detector demo cfg/coco.data yolov4-tiny.cfg yolov4-tiny.weights test.mp4 -ext_output -c 0 -mjpeg_port 8090 -dont_show```
    - using yolov4-tiny model
    - Video stream: 1280 x 720
    - FPS:128.1 	 AVG_FPS:123.7