In order to run the examples, first pull the streamproc docker image:
docker pull arcturusnetworks/streamproc:latest
Run a docker container from this image:
docker run -it --rm --net host -e UID=$(id -u) -e GID=$(id -g) arcturusnetworks/streamproc
Note: If running as root, specify a non-root user and group ID (any value greater than 0), for example:
docker run -it --rm --net host -e UID=1000 -e GID=1000 arcturusnetworks/streamproc
From within the container, build the examples:
cd /src/streamproc-examples
mkdir -p build && cd build && cmake ..
make -j4
The examples can now be executed:
./objdet
./embed
./reid
Access the output stream at http://ip_address:7777
Build them in order to run them
cd /src/deepvision-examples
mkdir -p build && cd build && cmake ..
make -j4
Run the objdet_example and perform inference using mobilenet_v2 trained on COCO using the armnn backend,
./objdet_example \
--objdet-model-file=../data/models/objdet/ssd_mobilenet_v2_coco_2018_03_29.tflite \
--in-image-dir=../data/images/ \
--backend=armnn \
--out-image-dir=/tmp
Run the objdet_example and perform inference using mobilenet_v3 trained on COCO using the tflite backend,
./objdet_example \
--objdet-model-file=../data/models/objdet/ssd_mobilenet_v3_small_coco_2020_01_14.tflite \
--in-image-dir=../data/images/ \
--backend=tflite\
--out-image-dir=/tmp
Run the yolo_v3_example
./yolo_v3_example ../data/models/objdet/yolo_v3_416x416_coco2014.tflite ../data/images/img2.jpg
Run the classify_example and perform image level classification using mobilenet_v2 trained on the Imagenet dataset,
./classify_example \
../data/models/classify/mobilenet_v2_1.0_224.tflite\
../data/models/classify/labels_mobilenet_quant_v1_224.txt\
../data/images/french_bulldog.png
[ArmNN]
Time: 93ms (10fps)
Classifications: [
Classification { label: French bulldog, conf: 94% },
Classification { label: Boston bull, conf: 0.16% },
Classification { label: pug, conf: 0.12% },
Classification { label: boxer, conf: 0.11% },
Classification { label: Scotch terrier, conf: 0.055% }]
[TFLite]
Time: 134ms (7fps)
Classifications: [
Classification { label: French bulldog, conf: 94% },
Classification { label: Boston bull, conf: 0.16% },
Classification { label: pug, conf: 0.12% },
Classification { label: boxer, conf: 0.11% },
Classification { label: Scotch terrier, conf: 0.055% }]
To generate documentation, issue the following commands
cd /usr/local/share/streamproc
sudo doxygen Doxyfile
The docs will be generated in the /usr/local/share/streamproc/html folder.
Note: This has only been tested on the Nvidia Jetson platform (specifically: Nano, Xavier NX)
docker run -it --runtime nvidia arcturusnetworks/streamproc:jetson-benchmarks bash -c "cd /src/deepvision-benchmarking; ./benchmarks"
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MODEL | FORMAT | BACKEND | SUPPORT | INFERENCE (ms) | FPS | TOTAL (ms) | FPS
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coco_ssd_mobilenet_v1_1.0_quant_2018_06_29 | tflite | ARMNN | YES | 82 | 12.20 | 90 | 11.11
coco_ssd_mobilenet_v1_1.0_quant_2018_06_29 | tflite | TFLITE | YES | 105 | 9.52 | 111 | 9.01
ssd_inception_v2_coco_2018_01_28 | tflite | ARMNN | YES | 1053 | 0.95 | 1055 | 0.95
ssd_inception_v2_coco_2018_01_28 | tflite | TFLITE | YES | 800 | 1.25 | 801 | 1.25
ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03 | tflite | ARMNN | YES | 92 | 10.87 | 93 | 10.75
ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03 | tflite | TFLITE | YES | 134 | 7.46 | 136 | 7.35
ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18 | tflite | ARMNN | NO | 0 | 0.00 | 0 | 0.00
ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18 | tflite | TFLITE | YES | 170 | 5.88 | 171 | 5.85
ssd_mobilenet_v1_coco_2018_01_28 | tflite | ARMNN | YES | 143 | 6.99 | 144 | 6.94
ssd_mobilenet_v1_coco_2018_01_28 | tflite | TFLITE | YES | 251 | 3.98 | 252 | 3.97
ssd_mobilenet_v2_coco_2018_03_29 | tflite | ARMNN | YES | 719 | 1.39 | 720 | 1.39
ssd_mobilenet_v2_coco_2018_03_29 | tflite | TFLITE | YES | 449 | 2.23 | 450 | 2.22
ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03 | tflite | ARMNN | YES | 93 | 10.75 | 99 | 10.10
ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03 | tflite | TFLITE | YES | 139 | 7.19 | 144 | 6.94
ssd_mobilenet_v3_large_coco_2019_08_14 | tflite | ARMNN | NO | 0 | 0.00 | 0 | 0.00
ssd_mobilenet_v3_large_coco_2019_08_14 | tflite | TFLITE | YES | 142 | 7.04 | 145 | 6.90
ssd_mobilenet_v3_small_coco_2019_08_14 | tflite | ARMNN | NO | 0 | 0.00 | 0 | 0.00
ssd_mobilenet_v3_small_coco_2019_08_14 | tflite | TFLITE | YES | 63 | 15.87 | 68 | 14.71
ssdlite_mobilenet_v2_coco_2018_05_09 | tflite | ARMNN | YES | 109 | 9.17 | 110 | 9.09
ssdlite_mobilenet_v2_coco_2018_05_09 | tflite | TFLITE | YES | 167 | 5.99 | 169 | 5.92
yolo_v3_416x416_coco2014 | tflite | ARMNN | NO | 0 | 0.00 | 0 | 0.00
yolo_v3_416x416_coco2014 | tflite | TFLITE | YES | 4969 | 0.20 | 4980 | 0.20
ssd_mobilenet_v1_coco | uff | TENSORRT | YES | 16 | 62.50 | 20 | 50.00
ssd_mobilenet_v2_coco | uff | TENSORRT | YES | 20 | 50.00 | 23 | 43.48
ssd_inception_v2_coco | uff | TENSORRT | YES | 23 | 43.48 | 28 | 35.71
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Content type
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Digest
sha256:5a9b2e0ff…
Size
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Last updated
24 days ago
docker pull arcturusnetworks/streamproc:amd64-person-demo-dev