examples

Tengine Lite's examples providing simple yet fancy demos.

Classification
Facial Landmark Detection
SSD Object Detection
RetinaFace Face Detection
Scrfd Face Detection
Yolact Instance Segmentation
U-Net Image Segmentation
YoloV3 Object Detection Task
YoloV4-tiny Object Detection Task
YoloV5s Object Detection Task
NanoDet Object Detection Task
EfficientDet Object Detection Task
Yolox Object Detection Task
OpenPose Human Pose Estimation Task
HRNet Human Pose Estimation Task
CRNN Chinese character recognition
PaddleSeg Human Segmentation

In addition to single-image single-model-inference example, Tengine Lite gives pipeline application based on video and image stream.

Distance Estimation
Facial Feature Extraction

Classification task - tm_classification.c

Tengine Lite is compatible with original Tengine's C API. Here we demonstrate how to run MobileNet v1 via tm_classification example code, providing image classification functionality. This would help you get involve with Tengine Lite C API. We use the popular tiger cat image for test.

Model Zoo

The model zoo contains the models, images, and files needed to run examples

Baidu Netdisk (password: 7ke5)
Google Drive

Reference Code

tm_classification.c

Compilation

build.sh compiles example folders demo programs on default. Take x86 platform as example, the compilation generated demos are stored in ./build/install/bin/ folder.

bug1989@DESKTOP-SGN0H2A:/mnt/d/ubuntu/gitlab/build-linux$ tree install
install
├── bin
│   ├── tm_alphapose
│   ├── tm_classification
│   ├── tm_classification_int8
│   ├── tm_classification_uint8
│   ├── tm_crnn
│   ├── tm_efficientdet
│   ├── tm_efficientdet_uint8
│   ├── tm_hrnet
│   ├── tm_landmark
│   ├── tm_landmark_uint8
│   ├── tm_mobilefacenet
│   ├── tm_mobilefacenet_uint8
│   ├── tm_mobilenet_ssd
│   ├── tm_mobilenet_ssd_uint8
│   ├── tm_nanodet_m
│   ├── tm_openpose
│   ├── tm_retinaface
│   ├── tm_scrfd
│   ├── tm_ultraface
│   ├── tm_unet
│   ├── tm_yolact
│   ├── tm_yolact_uint8
│   ├── tm_yolofastest
│   ├── tm_yolov3
│   ├── tm_yolov3_tiny
│   ├── tm_yolov3_tiny_uint8
│   ├── tm_yolov3_uint8
│   ├── tm_yolov4
│   ├── tm_yolov4_tiny
│   ├── tm_yolov4_tiny_uint8
│   ├── tm_yolov4_uint8
│   ├── tm_yolov5
│   └── tm_yolov5s
├── include
│   └── tengine
│       └── c_api.h
└── lib
    ├── libtengine-lite-static.a
    └── libtengine-lite.so

Running Result

Put testing images and models under root folder of Tengine-Lite project, and run it:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_classification -m models/mobilenet.tmfile -i images/cat.jpg -g 224,224 -s 0.017,0.017,0.017 -w 104.007,116.669,122.679

output:

tengine-lite library version: 1.4-dev

model file : models/mobilenet.tmfile
image file : images/cat.jpg
img_h, img_w, scale[3], mean[3] : 224 224 , 0.017 0.017 0.017, 104.0 116.7 122.7
Repeat 1 times, thread 1, avg time 33.74 ms, max_time 33.74 ms, min_time 33.74 ms
--------------------------------------
8.574144, 282
7.880117, 277
7.812573, 278
7.286458, 263
6.357486, 281
--------------------------------------

Facial Landmark Detection Task - tm_landmark.cpp

We use this image:

Run it with:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_landmark -m models/landmark.tmfile -i images/mobileface02.jpg -r 1 -t 1

output:

tengine-lite library version: 1.4-dev
Repeat [1] min 8.784 ms, max 8.784 ms, avg 8.784 ms

SSD Object Detection Task - tm_mobilenet_ssd.c

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_mobilenet_ssd -m models/mobilenet_ssd.tmfile -i images/ssd_dog.jpg -r 1 -t 1

output:

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 78.89 ms, max_time 78.89 ms, min_time 78.89 ms
--------------------------------------
detect result num: 3 
dog     :99.8%
BOX:( 138 , 209 ),( 324 , 541 )
car     :99.7%
BOX:( 467 , 72 ),( 687 , 171 )
bicycle :99.5%
BOX:( 107 , 141 ),( 574 , 415 )
======================================
[DETECTED IMAGE SAVED]:
======================================

RetinaFace Face Detection Task - tm_refinaface.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_retinaface -m models/retinaface.tmfile -i images/mtcnn_face4.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
img_h, img_w : 316, 474
Repeat 1 times, thread 1, avg time 28.78 ms, max_time 28.78 ms, min_time 28.78 ms
--------------------------------------
detected face num: 4
BOX 1.00:( 38.4053 , 86.142 ),( 46.3009 , 64.0174 )
BOX 0.99:( 384.076 , 56.9844 ),( 76.968 , 83.9609 )
BOX 0.99:( 169.196 , 87.1324 ),( 38.4133 , 46.8504 )
BOX 0.98:( 290.004 , 104.453 ),( 37.6346 , 46.7777 )

Scrfd Face Detection Task - tm_scrfd.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_scrfd -m models/scrfd_2.5g_kps.tmfile -i images/face5.jpg -r 1 -t 1

output：

tengine-lite library version: 1.5-dev
Repeat 1 times, thread 1, avg time 289.97 ms, max_time 289.97 ms, min_time 289.97 ms
--------------------------------------
detection num: 5
0.90917 at 199.37 54.92 28.52 x 38.12
0.89985 at 70.50 29.96 32.26 x 41.25
0.88838 at 111.36 48.00 33.53 x 46.77
0.88484 at 247.54 51.15 30.21 x 37.29
0.83953 at 149.23 49.48 27.89 x 38.50

Yolact Instance Segmentation Task - tm_yolact.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolact -m models/yolact.tmfile -i images/ssd_car.jpg -r 1 -t 1

output:

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 2064.44 ms, max_time 2064.44 ms, min_time 2064.44 ms
--------------------------------------
6 = 0.99966 at 130.82 57.77 340.78 x 237.36
3 = 0.99675 at 323.39 194.97 175.57 x 132.96
1 = 0.33431 at 191.24 195.78 103.06 x 179.22

U-Net Image Segmentation Task - tm_unet.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_unet -m models/unet_sim3.tmfile -i images/carvana01.jpg -r 1 -t 1

output:

Image height not specified, use default 512
Image width not specified, use default  512
Scale value not specified, use default  0.00392, 0.00392, 0.00392
tengine-lite library version: 1.4-dev

model file : models/unet_sim3.tmfile
image file : images/carvana01.jpg
img_h, img_w, scale[3], mean[3] : 512 512 , 0.004 0.004 0.004, 0.0 0.0 0.0
Repeat 1 times, thread 1, avg time 4861.93 ms, max_time 4861.93 ms, min_time 4861.93 ms
--------------------------------------
segmentation result is save as unet_out.png

YoloV3 Object Detection Task - tm_yolov3.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolov3 -m models/yolov3.tmfile -i images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 1131.67 ms, max_time 1131.67 ms, min_time 1131.67 ms
--------------------------------------
detection num: 3
16: 100%, [ 123,  223,  320,  544], dog
 1:  99%, [ 160,  117,  568,  435], bicycle
 7:  94%, [ 473,   87,  693,  166], truck

YoloV4-tiny Object Detection Task - tm_yolov4_tiny.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolov4_tiny -m models/yolov4-tiny.tmfile -i images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 152.50 ms, max_time 152.50 ms, min_time 152.50 ms
--------------------------------------
detection num: 3
16:  87%, [ 136,  206,  318,  542], dog
 7:  81%, [ 463,   79,  703,  170], truck
 1:  61%, [  72,  100,  577,  479], bicycle

YoloV5s Object Detection Task - tm_yolov5s.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolov5s -m models/yolov5s.tmfile -i images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 462.94 ms, max_time 462.94 ms, min_time 462.94 ms
--------------------------------------
detection num: 3
16:  89%, [ 135,  218,  313,  558], dog
 7:  86%, [ 472,   78,  689,  169], truck
 1:  75%, [ 123,  107,  578,  449], bicycle

NanoDet Object Detection Task - tm_nanodet_m.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_nanodet_m -m models/nanodet.tmfile -i images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 35.96 ms, max_time 35.96 ms, min_time 35.96 ms
--------------------------------------
detection num: 3
 1: 59.313%, [141.945, 160.890, 563.568, 429.829], bicycle
16: 50.605%, [132.646, 205.861, 312.255, 511.470], dog
 2: 48.931%, [462.477,  72.462, 701.777, 170.343], car

EfficientDet Object Detection Task - tm_efficientdet.c

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_efficientdet -m ../models/efficientdet.tmfile -i ../images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.4-dev
model file : ../models/efficientdet.tmfile
image file : ../images/ssd_dog.jpg
img_h, img_w, scale[3], mean[3] : 512 512 , 0.017 0.018 0.017, 123.7 116.3 103.5
Repeat 1 times, thread 1, avg time 598.86 ms, max_time 598.86 ms, min_time 598.86 ms
--------------------------------------
17:  80%, [ 132,  222,  315,  535], dog
 7:  73%, [ 467,   74,  694,  169], truck
 1:  42%, [ 103,  119,  555,  380], bicycle
 2:  29%, [ 687,  113,  724,  156], car
 2:  25%, [  57,   77,  111,  124], car

Yolox Object Detection Task - tm_yolox.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_yolox -m ../models/yolox_nano.tmfile -i ../images/ssd_dog.jpg -r 1 -t 1

output：

tengine-lite library version: 1.5-dev
Repeat 1 times, thread 1, avg time 97.64 ms, max_time 97.64 ms, min_time 97.64 ms
--------------------------------------
detection num: 3
16:  85%, [ 132,  216,  318,  545], dog
 1:  83%, [ 112,  140,  568,  427], bicycle
 2:  69%, [ 466,   77,  693,  168], car

OpenPose Human Pose Estimation Task - tm_openpose.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_openpose -m models/openpose_coco.tmfile -i images/pose.jpg -r 1 -t 1

output:

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 7296.71 ms, max_time 7296.71 ms, min_time 7296.71 ms
--------------------------------------
KeyPoints Coordinate:
0:[292.174, 55.6522]
1:[306.087, 125.217]
2:[250.435, 139.13]
3:[236.522, 222.609]
4:[222.609, 306.087]
5:[361.739, 125.217]
6:[403.478, 208.696]
7:[417.391, 292.174]
8:[264.348, 306.087]
9:[264.348, 431.304]
10:[264.348, 570.435]
11:[347.826, 306.087]
12:[375.652, 431.304]
13:[333.913, 542.609]
14:[278.261, 41.7391]
15:[306.087, 41.7391]
16:[264.348, 55.6522]
17:[320, 55.6522]

The result of human pose estimation will be saved as images, whose names are: Output-Keypionts.jpg and Output-Skeleton.jpg.

HRNet Human Pose Estimation Task - tm_hrnet.cpp

We use this image:

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_hrnet -m models/hrnet.tmfile -i images/pose.jpg -r 1 -t 1

output:

tengine-lite library version: 1.4-dev
Repeat [1] min 416.223 ms, max 416.223 ms, avg 416.223 ms
x: 27, y: 58, score: 0.91551
x: 27, y: 45, score: 0.865156
x: 28, y: 30, score: 0.831916
x: 34, y: 29, score: 0.839507
x: 38, y: 44, score: 0.88559
x: 35, y: 55, score: 0.891349
x: 31, y: 30, score: 0.873104
x: 31, y: 14, score: 0.928233
x: 30, y: 10, score: 0.948434
x: 29, y: 1, score: 0.915752
x: 23, y: 31, score: 0.811694
x: 24, y: 24, score: 0.935574
x: 24, y: 14, score: 0.899991
x: 37, y: 13, score: 0.908696
x: 41, y: 22, score: 0.902927
x: 41, y: 29, score: 0.847032

Chinese character recognition task - tm_crnn.cpp

model file:crnn_lite_dense.tmfile image file:o2_resize.jpg font file:keys.txt

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_crnn -m models/crnn_lite_dense.tmfile -i images/o2_resize.jpg -l files/keys.txt

result:

tengine-lite library version: 1.4-dev
Repeat 1 times, thread 1, avg time 23.30 ms, max_time 23.30 ms, min_time 23.30 ms
--------------------------------------
如何突破自己的颜值上限
--------------------------------------

The result of ocr recognition is displayed in terminal, you may also modify the source code to save it to file.

Human segmentation task - tm_seghuman.cpp

model file：paddleSegSim.tmfile

image file：human_image.jpg

image file：

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./build/install/bin/tm_seghuman -m models/paddleSegSim.tmfile -i images/human_image.jpeg

result：

tengine-lite library version: 1.5-dev
Repeat 1 times, avg time 123.766 ms, max_time 123.766 ms, min_time 123.766 ms

segmentation result image is saved as seg_human_result.jpg.

Distance Estimation

model file:mobilenet_ssd.tmfile

run (GPU recommended)

$ cd build/examples
$ ln -s models/mobilenet_ssd.tmfile
$ export LD_LIBRARY_PATH=./build/install/lib
$ ./tm_pipeline_estimate_ped_distance
detect result num: 1 
person	:100.0%
BOX:( 35 , 78 ),( 587 , 478 )
...

Facial Feature Extraction

model list:

rfb-320.tmfile face detection
landmark.tmfile face landmark
mobilefacenet.tmfile face feature

$ cd build/examples
$ ln -s models/rfb-320.tmfile
$ ln -s models/landmark.tmfile
$ ln -s models/mobilefacenet.tmfile

run (GPU recommanded):

$ export LD_LIBRARY_PATH=./build/install/lib
$ ./tm_pipeline_enroll_face  ./images

face feature would serialized to feature0.bin

We will continously updating more fancy demos, please stay tuned...

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README_EN.md

README_EN.md

examples

Classification task - tm_classification.c

Model Zoo

Reference Code

Compilation

Running Result

Facial Landmark Detection Task - tm_landmark.cpp

SSD Object Detection Task - tm_mobilenet_ssd.c

RetinaFace Face Detection Task - tm_refinaface.cpp

Scrfd Face Detection Task - tm_scrfd.cpp

Yolact Instance Segmentation Task - tm_yolact.cpp

U-Net Image Segmentation Task - tm_unet.cpp

YoloV3 Object Detection Task - tm_yolov3.cpp

YoloV4-tiny Object Detection Task - tm_yolov4_tiny.cpp

YoloV5s Object Detection Task - tm_yolov5s.cpp

NanoDet Object Detection Task - tm_nanodet_m.cpp

EfficientDet Object Detection Task - tm_efficientdet.c

Yolox Object Detection Task - tm_yolox.cpp

OpenPose Human Pose Estimation Task - tm_openpose.cpp

HRNet Human Pose Estimation Task - tm_hrnet.cpp

Chinese character recognition task - tm_crnn.cpp

Human segmentation task - tm_seghuman.cpp

Distance Estimation

Facial Feature Extraction

Files

README_EN.md

Latest commit

History

README_EN.md

File metadata and controls

examples

Classification task - tm_classification.c

Model Zoo

Reference Code

Compilation

Running Result

Facial Landmark Detection Task - tm_landmark.cpp

SSD Object Detection Task - tm_mobilenet_ssd.c

RetinaFace Face Detection Task - tm_refinaface.cpp

Scrfd Face Detection Task - tm_scrfd.cpp

Yolact Instance Segmentation Task - tm_yolact.cpp

U-Net Image Segmentation Task - tm_unet.cpp

YoloV3 Object Detection Task - tm_yolov3.cpp

YoloV4-tiny Object Detection Task - tm_yolov4_tiny.cpp

YoloV5s Object Detection Task - tm_yolov5s.cpp

NanoDet Object Detection Task - tm_nanodet_m.cpp

EfficientDet Object Detection Task - tm_efficientdet.c

Yolox Object Detection Task - tm_yolox.cpp

OpenPose Human Pose Estimation Task - tm_openpose.cpp

HRNet Human Pose Estimation Task - tm_hrnet.cpp

Chinese character recognition task - tm_crnn.cpp

Human segmentation task - tm_seghuman.cpp

Distance Estimation

Facial Feature Extraction