Running publicly available pre-trained models on Intel NCS 2
The really cool thing about the deep learning community is that they have published several pre-trained models for free. Let’s do our part in preventing global warming by not duplicating their efforts in re-training the same network on the same dataset that would consume power for a week or two. The below steps walk you through the toolkit’s development workflow of converting publicly available pre-trained neural net models to IR files and then running them on Intel NCS 2.
Run the following commands in a terminal window
On most Linux machines, hitting ctrl+alt+t will open a terminal window.
Step 1: Download publicly available models that are known to work with the toolkit
cd ~/intel/computer_vision_sdk/deployment_tools/model_downloader
# List public models that are known to work with OpenVINO
python3 downloader.py --print_all
# Download a specific model, say GoogLeNet V2
python3 downloader.py --name googlenet-v2
You can run downloader.py without the --name option to download all models, but it’ll take quite a while.
If the script ran fine, you should see googlenet-v2.caffemodel and googlenet-v2.prototxt in model_downloader/classification/googlenet/v2/caffe folder.
publicly available pre-trained models on Intel NCS 2
下載目錄：/opt/intel/computer_vision_sdk/deployment_tools/model_downloader
strong@foreverstrong:~$ cd /opt/intel/computer_vision_sdk/deployment_tools/model_downloader/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ ll
total 116
drwxr-xr-x 2 root root 4096 11月 20 09:17 ./
drwxr-xr-x 10 root root 4096 11月 20 09:17 ../
-rwxr-xr-x 1 root root 10200 11月 20 09:17 downloader.py*
-rw-r--r-- 1 root root 21338 11月 20 09:17 license.txt
-rw-r--r-- 1 root root 62585 11月 20 09:17 list_topologies.yml
-rw-r--r-- 1 root root 4463 11月 20 09:17 README.md
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ python3 downloader.py --print_all
densenet-121
densenet-161
densenet-169
densenet-201
squeezenet1.0
squeezenet1.1
mtcnn-p
mtcnn-r
mtcnn-o
mobilenet-ssd
vgg19
vgg16
ssd512
ssd300
inception-resnet-v2
dilation
googlenet-v1
googlenet-v2
googlenet-v4
alexnet
ssd_mobilenet_v2_coco
resnet-50
resnet-101
resnet-152
googlenet-v3
age-gender-recognition-retail-0013
age-gender-recognition-retail-0013-fp16
emotions-recognition-retail-0003
emotions-recognition-retail-0003-fp16
face-detection-adas-0001
face-detection-adas-0001-fp16
face-detection-retail-0004
face-detection-retail-0004-fp16
face-person-detection-retail-0002
face-person-detection-retail-0002-fp16
face-reidentification-retail-0001
face-reidentification-retail-0001-fp16
head-pose-estimation-adas-0001
head-pose-estimation-adas-0001-fp16
landmarks-regression-retail-0001
landmarks-regression-retail-0001-fp16
license-plate-recognition-barrier-0001
license-plate-recognition-barrier-0001-fp16
pedestrian-and-vehicle-detector-adas-0001
pedestrian-and-vehicle-detector-adas-0001-fp16
pedestrian-detection-adas-0002
pedestrian-detection-adas-0002-fp16
person-attributes-recognition-crossroad-0031
person-attributes-recognition-crossroad-0031-fp16
person-detection-action-recognition-0001
person-detection-action-recognition-0001-fp16
person-detection-retail-0001
person-detection-retail-0001-fp16
person-detection-retail-0013
person-detection-retail-0013-fp16
person-reidentification-retail-0031
person-reidentification-retail-0031-fp16
person-reidentification-retail-0076
person-reidentification-retail-0076-fp16
person-reidentification-retail-0079
person-reidentification-retail-0079-fp16
person-vehicle-bike-detection-crossroad-0078
person-vehicle-bike-detection-crossroad-0078-fp16
road-segmentation-adas-0001
road-segmentation-adas-0001-fp16
semantic-segmentation-adas-0001
semantic-segmentation-adas-0001-fp16
vehicle-attributes-recognition-barrier-0039
vehicle-attributes-recognition-barrier-0039-fp16
vehicle-detection-adas-0002
vehicle-detection-adas-0002-fp16
vehicle-license-plate-detection-barrier-0106
vehicle-license-plate-detection-barrier-0106-fp16
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ python3 downloader.py
###############|| Start downloading models ||###############
Traceback (most recent call last):
File "downloader.py", line 160, in <module>
os.makedirs(output, exist_ok=True)
File "/usr/lib/python3.5/os.py", line 231, in makedirs
makedirs(head, mode, exist_ok)
File "/usr/lib/python3.5/os.py", line 231, in makedirs
makedirs(head, mode, exist_ok)
File "/usr/lib/python3.5/os.py", line 231, in makedirs
makedirs(head, mode, exist_ok)
File "/usr/lib/python3.5/os.py", line 241, in makedirs
mkdir(name, mode)
PermissionError: [Errno 13] Permission denied: '/opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification'
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ cd ..
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools$ cd ..
strong@foreverstrong:/opt/intel/computer_vision_sdk$ sudo chmod 777 -R deployment_tools/
[sudo] password for strong:
strong@foreverstrong:/opt/intel/computer_vision_sdk$ cd deployment_tools/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools$ ll
total 40
drwxrwxrwx 10 root root 4096 11月 20 09:17 ./
drwxr-xr-x 11 root root 4096 11月 20 09:17 ../
drwxrwxrwx 6 root root 4096 11月 20 09:17 computer_vision_algorithms/
drwxrwxrwx 2 root root 4096 11月 20 09:17 demo/
drwxrwxrwx 3 root root 4096 11月 20 09:17 documentation/
drwxrwxrwx 4 root root 4096 11月 20 09:17 extension_generator/
drwxrwxrwx 9 root root 4096 11月 20 09:17 inference_engine/
drwxrwxrwx 29 root root 4096 11月 20 09:17 intel_models/
drwxrwxrwx 2 root root 4096 11月 20 09:17 model_downloader/
drwxrwxrwx 6 root root 4096 11月 20 09:17 model_optimizer/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools$ cd model_downloader/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ ll
total 116
drwxrwxrwx 2 root root 4096 11月 20 09:17 ./
drwxrwxrwx 10 root root 4096 11月 20 09:17 ../
-rwxrwxrwx 1 root root 10200 11月 20 09:17 downloader.py*
-rwxrwxrwx 1 root root 21338 11月 20 09:17 license.txt*
-rwxrwxrwx 1 root root 62585 11月 20 09:17 list_topologies.yml*
-rwxrwxrwx 1 root root 4463 11月 20 09:17 README.md*
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ python3 downloader.py
###############|| Start downloading models ||###############
...100%, 74 KB, 29896 KB/s, 0 seconds passed ========= densenet-121.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/121/caffe/densenet-121.prototxt
...100%, 99 KB, 89929 KB/s, 0 seconds passed ========= densenet-161.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/161/caffe/densenet-161.prototxt
...100%, 104 KB, 35823 KB/s, 0 seconds passed ========= densenet-169.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/169/caffe/densenet-169.prototxt
...100%, 124 KB, 69689 KB/s, 0 seconds passed ========= densenet-201.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/201/caffe/densenet-201.prototxt
...100%, 9 KB, 29780 KB/s, 0 seconds passed ========= squeezenet1.0.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/squeezenet/1.0/caffe/squeezenet1.0.prototxt
...100%, 9 KB, 103257 KB/s, 0 seconds passed ========= squeezenet1.1.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/squeezenet/1.1/caffe/squeezenet1.1.prototxt
...100%, 2 KB, 9029 KB/s, 0 seconds passed ========= mtcnn-p.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/p/caffe/mtcnn-p.prototxt
...100%, 3 KB, 43328 KB/s, 0 seconds passed ========= mtcnn-r.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/r/caffe/mtcnn-r.prototxt
...100%, 3 KB, 43027 KB/s, 0 seconds passed ========= mtcnn-o.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/o/caffe/mtcnn-o.prototxt
...100%, 28 KB, 54100 KB/s, 0 seconds passed ========= mobilenet-ssd.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mobilenet-ssd/caffe/mobilenet-ssd.prototxt
...100%, 5 KB, 19903 KB/s, 0 seconds passed ========= vgg19.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/vgg/19/caffe/vgg19.prototxt
...100%, 4 KB, 17993 KB/s, 0 seconds passed ========= vgg16.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/vgg/16/caffe/vgg16.prototxt
...100%, 187 KB, 554 KB/s, 0 seconds passed ========= inception-resnet-v2.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/inception-resnet/v2/caffe/inception-resnet-v2.prototxt
...100%, 9 KB, 69444 KB/s, 0 seconds passed ========= dilation.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/semantic_segmentation/dilation/cityscapes/caffe/dilation.prototxt
...100%, 35 KB, 3242 KB/s, 0 seconds passed ========= googlenet-v1.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v1/caffe/googlenet-v1.prototxt
...100%, 58 KB, 61925 KB/s, 0 seconds passed ========= googlenet-v2.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.prototxt
...100%, 84 KB, 390 KB/s, 0 seconds passed ========= googlenet-v4.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v4/caffe/googlenet-v4.prototxt
...100%, 3 KB, 13036 KB/s, 0 seconds passed ========= alexnet.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/alexnet/caffe/alexnet.prototxt
...100%, 31 KB, 58993 KB/s, 0 seconds passed ========= resnet-50.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/resnet/v1/50/caffe/resnet-50.prototxt
...100%, 63 KB, 77066 KB/s, 0 seconds passed ========= resnet-101.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/resnet/v1/101/caffe/resnet-101.prototxt
...100%, 95 KB, 70622 KB/s, 0 seconds passed ========= resnet-152.prototxt ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/resnet/v1/152/caffe/resnet-152.prototxt
...100%, 13 KB, 8055 KB/s, 0 seconds passed ========= age-gender-recognition-retail-0013.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013.xml
...100%, 13 KB, 7579 KB/s, 0 seconds passed ========= age-gender-recognition-retail-0013-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013-fp16.xml
...100%, 18 KB, 5643 KB/s, 0 seconds passed ========= emotions-recognition-retail-0003.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003.xml
...100%, 18 KB, 5371 KB/s, 0 seconds passed ========= emotions-recognition-retail-0003-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003-fp16.xml
...100%, 90 KB, 221 KB/s, 0 seconds passed ========= face-detection-adas-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.xml
...100%, 90 KB, 294 KB/s, 0 seconds passed ========= face-detection-adas-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001-fp16.xml
...100%, 47 KB, 183 KB/s, 0 seconds passed ========= face-detection-retail-0004.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/face-detection-retail-0004.xml
...100%, 47 KB, 226 KB/s, 0 seconds passed ========= face-detection-retail-0004-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/face-detection-retail-0004-fp16.xml
...100%, 164 KB, 374 KB/s, 0 seconds passed ========= face-person-detection-retail-0002.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002.xml
...100%, 163 KB, 438 KB/s, 0 seconds passed ========= face-person-detection-retail-0002-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002-fp16.xml
...100%, 148 KB, 353 KB/s, 0 seconds passed ========= face-reidentification-retail-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/face/rmnet_based/dldt/face-reidentification-retail-0001.xml
...100%, 148 KB, 409 KB/s, 0 seconds passed ========= face-reidentification-retail-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/face/rmnet_based/dldt/face-reidentification-retail-0001-fp16.xml
...100%, 16 KB, 5615 KB/s, 0 seconds passed ========= head-pose-estimation-adas-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001.xml
...100%, 16 KB, 8040 KB/s, 0 seconds passed ========= head-pose-estimation-adas-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001-fp16.xml
...100%, 16 KB, 6522 KB/s, 0 seconds passed ========= landmarks-regression-retail-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/landmarks_regression/0001/dldt/landmarks-regression-retail-0001.xml
...100%, 16 KB, 4944 KB/s, 0 seconds passed ========= landmarks-regression-retail-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_attributes/landmarks_regression/0001/dldt/landmarks-regression-retail-0001-fp16.xml
...100%, 22 KB, 181 KB/s, 0 seconds passed ========= license-plate-recognition-barrier-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/optical_character_recognition/license_plate/dldt/license-plate-recognition-barrier-0001.xml
...100%, 22 KB, 1819 KB/s, 0 seconds passed ========= license-plate-recognition-barrier-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/optical_character_recognition/license_plate/dldt/license-plate-recognition-barrier-0001-fp16.xml
...100%, 88 KB, 446 KB/s, 0 seconds passed ========= pedestrian-and-vehicle-detector-adas-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/pedestrian-and-vehicle/mobilenet-reduced-ssd/dldt/pedestrian-and-vehicle-detector-adas-0001.xml
...100%, 88 KB, 2012 KB/s, 0 seconds passed ========= pedestrian-and-vehicle-detector-adas-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/pedestrian-and-vehicle/mobilenet-reduced-ssd/dldt/pedestrian-and-vehicle-detector-adas-0001-fp16.xml
...100%, 88 KB, 2489 KB/s, 0 seconds passed ========= pedestrian-detection-adas-0002.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/pedestrian/mobilenet-reduced-ssd/dldt/pedestrian-detection-adas-0002.xml
...100%, 88 KB, 2704 KB/s, 0 seconds passed ========= pedestrian-detection-adas-0002-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/pedestrian/mobilenet-reduced-ssd/dldt/pedestrian-detection-adas-0002-fp16.xml
...100%, 95 KB, 2808 KB/s, 0 seconds passed ========= person-attributes-recognition-crossroad-0031.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_attributes/pedestrian/person-attributes-recognition-crossroad-0031/dldt/person-attributes-recognition-crossroad-0031.xml
...100%, 95 KB, 2694 KB/s, 0 seconds passed ========= person-attributes-recognition-crossroad-0031-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_attributes/pedestrian/person-attributes-recognition-crossroad-0031/dldt/person-attributes-recognition-crossroad-0031-fp16.xml
...100%, 255 KB, 1176 KB/s, 0 seconds passed ========= person-detection-action-recognition-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/action_detection/pedestrian/rmnet_ssd/0023/dldt/person-detection-action-recognition-0001.xml
...100%, 254 KB, 1279 KB/s, 0 seconds passed ========= person-detection-action-recognition-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/action_detection/pedestrian/rmnet_ssd/0023/dldt/person-detection-action-recognition-0001-fp16.xml
...100%, 131 KB, 1116 KB/s, 0 seconds passed ========= person-detection-retail-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0001.xml
...100%, 131 KB, 541 KB/s, 0 seconds passed ========= person-detection-retail-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0001-fp16.xml
...100%, 157 KB, 396 KB/s, 0 seconds passed ========= person-detection-retail-0013.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/pedestrian/rmnet_ssd/0013/dldt/person-detection-retail-0013.xml
...100%, 157 KB, 243 KB/s, 0 seconds passed ========= person-detection-retail-0013-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_detection/pedestrian/rmnet_ssd/0013/dldt/person-detection-retail-0013-fp16.xml
...100%, 66 KB, 184 KB/s, 0 seconds passed ========= person-reidentification-retail-0031.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0031/dldt/person-reidentification-retail-0031.xml
...100%, 66 KB, 185 KB/s, 0 seconds passed ========= person-reidentification-retail-0031-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0031/dldt/person-reidentification-retail-0031-fp16.xml
...100%, 152 KB, 317 KB/s, 0 seconds passed ========= person-reidentification-retail-0076.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0076/dldt/person-reidentification-retail-0076.xml
...100%, 152 KB, 354 KB/s, 0 seconds passed ========= person-reidentification-retail-0076-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0076/dldt/person-reidentification-retail-0076-fp16.xml
...100%, 151 KB, 369 KB/s, 0 seconds passed ========= person-reidentification-retail-0079.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0079/dldt/person-reidentification-retail-0079.xml
...100%, 151 KB, 382 KB/s, 0 seconds passed ========= person-reidentification-retail-0079-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/object_reidentification/pedestrian/rmnet_based/0079/dldt/person-reidentification-retail-0079-fp16.xml
...100%, 171 KB, 538 KB/s, 0 seconds passed ========= person-vehicle-bike-detection-crossroad-0078.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/crossroad/0078/dldt/person-vehicle-bike-detection-crossroad-0078.xml
...100%, 170 KB, 449 KB/s, 0 seconds passed ========= person-vehicle-bike-detection-crossroad-0078-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/crossroad/0078/dldt/person-vehicle-bike-detection-crossroad-0078-fp16.xml
...100%, 179 KB, 483 KB/s, 0 seconds passed ========= road-segmentation-adas-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/segmentation/curbs/dldt/road-segmentation-adas-0001.xml
...100%, 179 KB, 443 KB/s, 0 seconds passed ========= road-segmentation-adas-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/segmentation/curbs/dldt/road-segmentation-adas-0001-fp16.xml
...100%, 92 KB, 2541 KB/s, 0 seconds passed ========= semantic-segmentation-adas-0001.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/segmentation/semantic_segmentation/icnet_icv/dldt/semantic-segmentation-adas-0001.xml
...100%, 92 KB, 2482 KB/s, 0 seconds passed ========= semantic-segmentation-adas-0001-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/segmentation/semantic_segmentation/icnet_icv/dldt/semantic-segmentation-adas-0001-fp16.xml
...100%, 16 KB, 13206 KB/s, 0 seconds passed ========= vehicle-attributes-recognition-barrier-0039.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_attributes/vehicle/resnet10_update_1/dldt/vehicle-attributes-recognition-barrier-0039.xml
...100%, 16 KB, 8419 KB/s, 0 seconds passed ========= vehicle-attributes-recognition-barrier-0039-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_attributes/vehicle/resnet10_update_1/dldt/vehicle-attributes-recognition-barrier-0039-fp16.xml
...100%, 79 KB, 2625 KB/s, 0 seconds passed ========= vehicle-detection-adas-0002.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002.xml
...100%, 79 KB, 2571 KB/s, 0 seconds passed ========= vehicle-detection-adas-0002-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002-fp16.xml
...100%, 93 KB, 2874 KB/s, 0 seconds passed ========= vehicle-license-plate-detection-barrier-0106.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/barrier/0106/dldt/vehicle-license-plate-detection-barrier-0106.xml
...100%, 93 KB, 2746 KB/s, 0 seconds passed ========= vehicle-license-plate-detection-barrier-0106-fp16.xml ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/barrier/0106/dldt/vehicle-license-plate-detection-barrier-0106-fp16.xml
###############|| Start downloading weights ||###############
...100%, 31546 KB, 1910 KB/s, 16 seconds passed ========= densenet-121.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/121/caffe/densenet-121.caffemodel
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...100%, 55964 KB, 1938 KB/s, 28 seconds passed ========= densenet-169.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/densenet/169/caffe/densenet-169.caffemodel
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...100%, 4884 KB, 2224 KB/s, 2 seconds passed ========= squeezenet1.0.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/squeezenet/1.0/caffe/squeezenet1.0.caffemodel
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...100%, 27 KB, 277 KB/s, 0 seconds passed ========= mtcnn-p.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/p/caffe/mtcnn-p.caffemodel
...100%, 398 KB, 848 KB/s, 0 seconds passed ========= mtcnn-r.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/r/caffe/mtcnn-r.caffemodel
...100%, 1521 KB, 1757 KB/s, 0 seconds passed ========= mtcnn-o.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/mtcnn/o/caffe/mtcnn-o.caffemodel
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...100%, 540461 KB, 2820 KB/s, 191 seconds passed ========= vgg16.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/vgg/16/caffe/vgg16.caffemodel
...100%, 218272 KB, 3095 KB/s, 70 seconds passed ========= inception-resnet-v2.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/inception-resnet/v2/caffe/inception-resnet-v2.caffemodel
...100%, 524898 KB, 1068 KB/s, 491 seconds passed ========= dilation.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/semantic_segmentation/dilation/cityscapes/caffe/dilation.caffemodel
...100%, 52279 KB, 3417 KB/s, 15 seconds passed ========= googlenet-v1.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v1/caffe/googlenet-v1.caffemodel
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...100%, 238146 KB, 2369 KB/s, 100 seconds passed ========= alexnet.caffemodel ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/alexnet/caffe/alexnet.caffemodel
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...100%, 4113 KB, 3838 KB/s, 1 seconds passed ========= face-detection-adas-0001.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.bin
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...100%, 7466 KB, 3905 KB/s, 1 seconds passed ========= head-pose-estimation-adas-0001.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001.bin
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...100%, 4574 KB, 3781 KB/s, 1 seconds passed ========= person-detection-action-recognition-0001.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Retail/action_detection/pedestrian/rmnet_ssd/0023/dldt/person-detection-action-recognition-0001.bin
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...100%, 4603 KB, 3825 KB/s, 1 seconds passed ========= person-vehicle-bike-detection-crossroad-0078.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/crossroad/0078/dldt/person-vehicle-bike-detection-crossroad-0078.bin
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...100%, 719 KB, 1587 KB/s, 0 seconds passed ========= road-segmentation-adas-0001.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/segmentation/curbs/dldt/road-segmentation-adas-0001.bin
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...100%, 2445 KB, 2709 KB/s, 0 seconds passed ========= vehicle-attributes-recognition-barrier-0039.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_attributes/vehicle/resnet10_update_1/dldt/vehicle-attributes-recognition-barrier-0039.bin
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...100%, 4213 KB, 3444 KB/s, 1 seconds passed ========= vehicle-detection-adas-0002.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002.bin
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...100%, 2512 KB, 3377 KB/s, 0 seconds passed ========= vehicle-license-plate-detection-barrier-0106.bin ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/Security/object_detection/barrier/0106/dldt/vehicle-license-plate-detection-barrier-0106.bin
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###############|| Start downloading topologies in tarballs ||###############
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...100%, 86590 KB, 3430 KB/s, 25 seconds passed ========= googlenet-v3.tar.gz ====> /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v3/tf/googlenet-v3.tar.gz
###############|| Post processing ||###############
========= Changing input dimensions in squeezenet1.0.prototxt =========
========= Changing input dimensions in squeezenet1.1.prototxt =========
========= Changing input dimensions in mtcnn-p.prototxt =========
========= Changing input dimensions in vgg19.prototxt =========
========= Changing input dimensions in vgg16.prototxt =========
========= Extracting files from ssd512.tar.gz
========= Moving ssd512.prototxt and ssd512.caffemodel to /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/ssd/512/caffe after untarring the archive =========
========= Deleting "save_output_param" from ssd512.prototxt =========
========= Extracting files from ssd300.tar.gz
========= Moving ssd300.prototxt and ssd300.caffemodel to /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/ssd/300/caffe after untarring the archive =========
========= Deleting "save_output_param" from ssd300.prototxt =========
========= Changing input dimensions in googlenet-v1.prototxt =========
========= Changing input dimensions in googlenet-v2.prototxt =========
========= Moving to new Caffe layer presentation googlenet-v2.prototxt =========
========= Changing input dimensions in alexnet.prototxt =========
========= Extracting files from ssd_mobilenet_v2_coco.tar.gz
========= Moving ssd_mobilenet_v2_coco.frozen.pb to /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/object_detection/common/ssd_mobilenet_v2_coco/tf after untarring the archive =========
========= Extracting files from googlenet-v3.tar.gz
========= Moving googlenet-v3.frozen.pb to /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v3/tf/ after untarring the archive =========
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ ll
total 140
drwxrwxrwx 8 root root 4096 11月 28 11:59 ./
drwxrwxrwx 10 root root 4096 11月 20 09:17 ../
drwxrwxr-x 9 strong strong 4096 11月 28 11:29 classification/
-rwxrwxrwx 1 root root 10200 11月 20 09:17 downloader.py*
-rwxrwxrwx 1 root root 21338 11月 20 09:17 license.txt*
-rwxrwxrwx 1 root root 62585 11月 20 09:17 list_topologies.yml*
drwxrwxr-x 3 strong strong 4096 11月 28 11:29 object_detection/
-rwxrwxrwx 1 root root 4463 11月 20 09:17 README.md*
drwxrwxr-x 6 strong strong 4096 11月 28 11:29 Retail/
drwxrwxr-x 5 strong strong 4096 11月 28 11:30 Security/
drwxrwxr-x 3 strong strong 4096 11月 28 11:29 semantic_segmentation/
drwxrwxr-x 5 strong strong 4096 11月 28 11:30 Transportation/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$
license.txt
Configuration file for public topologies downloader includes following models:
==================================================================================================
* densenet-121, densenet-161, densenet-169, densenet-201 - Densely Connected Convolutional Networks https://github.com/shicai/DenseNet-Caffe
License terms:
Copyright (c) 2016, Zhuang Liu.
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name DenseNet nor the names of its contributors may be used to
endorse or promote products derived from this software without specific
prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
==================================================================================================
* squeezenet1.0, squeezenet1.1 - SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$0.5MB model size https://github.com/DeepScale/SqueezeNet
License terms:
BSD LICENSE.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions
and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
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IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
==================================================================================================
* mtcnn-p, mtcnn-r, mtcnn-o - Multi-task Cascaded Convolutional Networks: Proposal, Refine, Output https://github.com/DuinoDu/mtcnn/tree/master/model https://arxiv.org/ftp/arxiv/papers/1604/1604.02878.pdf
License terms:
MIT License
Copyright (c) 2016 Kaipeng Zhang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==================================================================================================
* mobilenet-ssd – Common object detection architecture https://github.com/chuanqi305/MobileNet-SSD
License terms:
Copyright 2016 The TensorFlow Authors. All rights reserved.
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==================================================================================================
* vgg16, vgg19 - Very Deep Convolutional Networks For Large-Scale Image Recognition https://arxiv.org/pdf/1409.1556.pdf
License terms:
COPYRIGHT
All contributions by François Chollet:
Copyright (c) 2015 - 2018, François Chollet.
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LICENSE
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Permission is hereby granted, free of charge, to any person obtaining a copy
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in the Software without restriction, including without limitation the rights
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SOFTWARE.
==================================================================================================
* ssd300, ssd512 - Single Shot MultiBox Detector https://arxiv.org/pdf/1512.02325.pdf
License terms:
COPYRIGHT
All new contributions compared to the original branch:
Copyright (c) 2015, 2016 Wei Liu (UNC Chapel Hill), Dragomir Anguelov (Zoox),
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Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
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list_topologies.yml
# Copyright (c) 2018 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
topologies:
#
# PUBLIC TOPOLOGIES
#
- name: "densenet-121"
description: "DenseNet-121 (https://github.com/shicai/DenseNet-Caffe). The following Caffe patch must be used to be able to convert this model: https://github.com/BVLC/caffe/pull/3057/files"
model: https://raw.githubusercontent.com/shicai/DenseNet-Caffe/a68651c0b91d8dcb7c0ecd39d1fc76da523baf8a/DenseNet_121.prototxt
model_hash: baeed2a423794c2c8dc1a80ad96e961112224fa1d319d535735ba93a2b535170
weights: https://drive.google.com/uc?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE&export=download
weights_hash: c6a6ec988d76c468c3f67501a23a39ec7bf6ebe6729fd99496a15d0e845478b2
output: "classification/densenet/121/caffe/"
weights_google_drive_id: 0B7ubpZO7HnlCcHlfNmJkU2VPelE
weights_size: 32303870
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.94,116.78,123.68] --scale_values data[58.8235294117647] --output fc6 --input_model <densenet-121.caffemodel> --input_proto <densenet-121.prototxt>"
framework: caffe
license: https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE
- name: "densenet-161"
description: "DenseNet-161 (https://github.com/shicai/DenseNet-Caffe). The following Caffe patch must be used to be able to convert this model: https://github.com/BVLC/caffe/pull/3057/files"
model: https://raw.githubusercontent.com/shicai/DenseNet-Caffe/a68651c0b91d8dcb7c0ecd39d1fc76da523baf8a/DenseNet_161.prototxt
model_hash: a193e029d66112b077ed29e8b8d36d0bae0593a7f3c64125a433937b5f035b69
weights: https://drive.google.com/uc?export=download&confirm=qlti&id=0B7ubpZO7HnlCa0phRGJIRERoTXM
weights_hash: e124d9a8f2284f4ab160569139217f709f21be6fc132c865b6a55cb8cae7d6b5
output: "classification/densenet/161/caffe"
weights_google_drive_id: 0B7ubpZO7HnlCa0phRGJIRERoTXM
weights_size: 115676075
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.94,116.78,123.68] --scale_values data[58.8235294117647] --output fc6 --input_model <densenet-161.caffemodel> --input_proto <densenet-161.prototxt>"
framework: caffe
license: https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE
- name: "densenet-169"
description: "DenseNet-169 (https://github.com/shicai/DenseNet-Caffe). The following Caffe patch must be used to be able to convert this model: https://github.com/BVLC/caffe/pull/3057/files"
model: https://raw.githubusercontent.com/shicai/DenseNet-Caffe/a68651c0b91d8dcb7c0ecd39d1fc76da523baf8a/DenseNet_169.prototxt
model_hash: dfb577c17d67327be70dba8f2810dbeec4f7edb836779c8055ec8f408b0a2cbc
weights: https://drive.google.com/uc?id=0B7ubpZO7HnlCRWVVdUJjVVAyQXc&export=download
weights_hash: 8d45f2ab15f6329e2f80004692c58129cc4875ffe43ddf59433ebc2216189f15
output: "classification/densenet/169/caffe"
weights_google_drive_id: 0B7ubpZO7HnlCRWVVdUJjVVAyQXc
weights_size: 57307526
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.94,116.78,123.68] --scale_values data[58.8235294117647] --output fc6 --input_model <densenet-169.caffemodel> --input_proto <densenet-169.prototxt>"
framework: caffe
license: https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE
- name: "densenet-201"
description: "DenseNet-201 (https://github.com/shicai/DenseNet-Caffe). The following Caffe patch must be used to be able to convert this model: https://github.com/BVLC/caffe/pull/3057/files"
model: https://raw.githubusercontent.com/shicai/DenseNet-Caffe/a68651c0b91d8dcb7c0ecd39d1fc76da523baf8a/DenseNet_201.prototxt
model_hash: 8edf61f867491315bc3780a41f74392c2a31042d20932eb145424a2dc8c6f8f7
weights: https://drive.google.com/uc?export=download&confirm=JWFY&id=0B7ubpZO7HnlCV3pud2oyR3lNMWs
weights_hash: ba464965293d4dd5557085d57ec810bf353de362dd90e1b8293e6f0707978e4a
output: "classification/densenet/201/caffe"
weights_google_drive_id: 0B7ubpZO7HnlCV3pud2oyR3lNMWs
weights_size: 81062969
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.94,116.78,123.68] --scale_values data[58.8235294117647] --output fc6 --input_model <densenet-201.caffemodel> --input_proto <densenet-201.prototxt>"
framework: caffe
license: https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE
- name: "squeezenet1.0"
description: "SqueezeNet v1.0 (https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.0)"
model: https://raw.githubusercontent.com/DeepScale/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/deploy.prototxt
model_hash: 6e4ecef2a27347e226a5ef8be31d6d1b9d19f5a40afa1986ec259fd5fa3bd91c
weights: https://github.com/DeepScale/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel
weights_hash: 9ff8035aada1f9ffa880b35252680d971434b141ec9fbacbe88309f0f9a675ce
output: "classification/squeezenet/1.0/caffe"
old_dims: [10,3,227,227]
new_dims: [1,3,227,227]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,227,227] --input data --mean_values data[104.0,117.0,123.0] --output prob --input_model <squeezenet1.0.caffemodel> --input_proto <squeezenet1.0.prototxt>"
framework: caffe
license: https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE
- name: "squeezenet1.1"
description: "SqueezeNet v1.1 (https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1)"
model: https://raw.githubusercontent.com/DeepScale/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/deploy.prototxt
model_hash: d041bfb2ab4b32fda4ff6c6966684132f2924e329916aa5bfe9285c6b23e3d1c
weights: https://github.com/DeepScale/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel
weights_hash: 72b912ace512e8621f8ff168a7d72af55910d3c7c9445af8dfbff4c2ee960142
output: "classification/squeezenet/1.1/caffe"
old_dims: [10,3,227,227]
new_dims: [1,3,227,227]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,227,227] --input data --mean_values data[104.0,117.0,123.0] --output prob --input_model <squeezenet1.1.caffemodel> --input_proto <squeezenet1.1.prototxt>"
framework: caffe
license: https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE
- name: "mtcnn-p"
description: "MTCNN-Proposal Network (https://github.com/DuinoDu/mtcnn/tree/master/model https://arxiv.org/ftp/arxiv/papers/1604/1604.02878.pdf)"
model: https://raw.githubusercontent.com/DuinoDu/mtcnn/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det1.prototxt
model_hash: adc1756d8515d3ca3a6a186c0fadab66fcae04bd8d3c6388e2fe8797a626dde4
weights: https://github.com/DuinoDu/mtcnn/raw/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det1.caffemodel
weights_hash: d6085e7f48ba7e6b6f1b58964595f6bce5b97bcc4866751f7b4bdc98f920c096
output: "object_detection/common/mtcnn/p/caffe"
old_dims: [1,3,12,12]
new_dims: [1,3,720,1280]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,720,1280] --input data --output prob1 --input_model <mtcnn-p.caffemodel> --input_proto <mtcnn-p.prototxt>"
framework: caffe
license: https://github.com/DuinoDu/mtcnn/blob/master/LICENSE
- name: "mtcnn-r"
description: "MTCNN-Refine Network (https://github.com/DuinoDu/mtcnn/tree/master/model https://arxiv.org/ftp/arxiv/papers/1604/1604.02878.pdf)"
model: https://raw.githubusercontent.com/DuinoDu/mtcnn/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det2.prototxt
model_hash: 077686e89e606354f425366afdb2018777d93c6450b50e2c12301f8a97f6bb47
weights: https://github.com/DuinoDu/mtcnn/raw/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det2.caffemodel
weights_hash: 39b20f7a57bb8176cc9466cea4dfd52da6a6f876de60c7ab222a309f2d0ca08c
output: "object_detection/common/mtcnn/r/caffe"
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,24,24] --input data --output prob1 --input_model <mtcnn-r.caffemodel> --input_proto <mtcnn-r.prototxt>"
framework: caffe
license: https://github.com/DuinoDu/mtcnn/blob/master/LICENSE
- name: "mtcnn-o"
description: "MTCNN-Output Network (https://github.com/DuinoDu/mtcnn/tree/master/model https://arxiv.org/ftp/arxiv/papers/1604/1604.02878.pdf)"
model: https://raw.githubusercontent.com/DuinoDu/mtcnn/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det3.prototxt
model_hash: a8385a3aad241acf5902b79466f9a359ea9f03a3b6dcbe1e1efa050908cf7d04
weights: https://github.com/DuinoDu/mtcnn/raw/db5bd8f02023f8d37913140fd2bf2749c2dbf266/model/det3.caffemodel
weights_hash: 9d6098829a4d6d318f37cec42142465637fafe4c673f2e93b69495bf7ca23d2d
output: "object_detection/common/mtcnn/o/caffe"
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,48,48] --input data --output prob1 --input_model <mtcnn-o.caffemodel> --input_proto <mtcnn-o.prototxt>"
framework: caffe
license: https://github.com/DuinoDu/mtcnn/blob/master/LICENSE
- name: "mobilenet-ssd"
description: "Common object detection architecture (https://github.com/chuanqi305/MobileNet-SSD)"
model: https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/ba00fc987b3eb0ba87bb99e89bf0298a2fd10765/MobileNetSSD_deploy.prototxt
model_hash: e781559c4f5beaec2a486ccd952af5b6fa408e9498761bf5f4fb80b4e9f0d25e
weights: https://drive.google.com/uc?id=0B3gersZ2cHIxRm5PMWRoTkdHdHc&export=download
weights_hash: 761c86fbae3d8361dd454f7c740a964f62975ed32f4324b8b85994edec30f6af
output: "object_detection/common/mobilenet-ssd/caffe"
weights_google_drive_id: 0B3gersZ2cHIxRm5PMWRoTkdHdHc
weights_size: 23147564
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,300,300] --input data --mean_values data[127.5,127.5,127.5] --scale_values data[127.50223128904757] --output detection_out --input_model <mobilenet-ssd.caffemodel> --input_proto <mobilenet-ssd.prototxt>"
framework: caffe
license: https://github.com/tensorflow/models/blob/master/LICENSE
- name: "vgg19"
description: "VGG Net-E (https://arxiv.org/pdf/1409.1556.pdf)"
model: https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt
model_hash: e7c9da46d837dd91049ddab46a57933a488e3d8a42162cdef51ab1edeff99725
weights: http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel
weights_hash: 31b4c627c40c6ee151325f079b7ac557fddbd1f79af932888796127a4f4f6954
output: "classification/vgg/19/caffe"
old_dims: [10,3,224,224]
new_dims: [1,3,224,224]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.939,116.779,123.68] --output prob --input_model <vgg19.caffemodel> --input_proto <vgg19.prototxt>"
framework: caffe
license: https://github.com/keras-team/keras/blob/master/LICENSE
- name: "vgg16"
description: "VGG Net-D (https://arxiv.org/pdf/1409.1556.pdf)"
model: https://gist.githubusercontent.com/ksimonyan/211839e770f7b538e2d8/raw/0067c9b32f60362c74f4c445a080beed06b07eb3/VGG_ILSVRC_16_layers_deploy.prototxt
model_hash: 9fdb04e3f5b9224af473e82441b05b76368dfa6dc841e80655303823e9770962
weights: http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
weights_hash: a6196bc498e45ea4cb2637114ae8db9410cf1556fd60a55ae93272371beba197
output: "classification/vgg/16/caffe"
old_dims: [10,3,224,224]
new_dims: [1,3,224,224]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[103.939,116.779,123.68] --output prob --input_model <vgg16.caffemodel> --input_proto <vgg16.prototxt>"
framework: caffe
license: https://github.com/keras-team/keras/blob/master/LICENSE
- name: "ssd512"
description: "SSD-512 (https://arxiv.org/pdf/1512.02325.pdf)"
model_hash: e8a18363aeb8a74a90fda6d6c8596994aca018061765b2aa38f8e0a580ca4c70
weights_hash: 60670be0b28cbde887c99d01c8aa5b10c9e1b7ae6e572da0901aa632da678b3b
output: "object_detection/common/ssd/512/caffe"
tar: https://drive.google.com/uc?export=download&confirm=MPr8&id=0BzKzrI_SkD1_MjFjNTlnempHNWs
tar_google_drive_id: 0BzKzrI_SkD1_MjFjNTlnempHNWs
model_path_prefix: "models/VGGNet/VOC0712Plus/SSD_512x512/deploy.prototxt"
weights_path_prefix: "models/VGGNet/VOC0712Plus/SSD_512x512/VGG_VOC0712Plus_SSD_512x512_iter_240000.caffemodel"
delete_output_param: True
tar_size: 100991061
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,512,512] --input data --mean_values data[104.0,117.0,123.0] --output detection_out --input_model <ssd512.caffemodel> --input_proto <ssd512.prototxt>"
framework: caffe
license: https://github.com/weiliu89/caffe/blob/ssd/LICENSE
- name: "ssd300"
description: "SSD-300 (https://arxiv.org/pdf/1512.02325.pdf)"
model_hash: 0366cd7cb909aebe499bc23d34e1aaf5c81ff2e32c438ba87db2845c4edf1309
weights_hash: 08cab67129496a99c3ef6ca9ae52a955e8ec770075a9d7e15ac8bf0b14c02ef1
output: "object_detection/common/ssd/300/caffe"
tar: https://drive.google.com/uc?export=download&confirm=YQb1&id=0BzKzrI_SkD1_TkFPTEQ1Z091SUE
tar_google_drive_id: 0BzKzrI_SkD1_TkFPTEQ1Z091SUE
model_path_prefix: "models/VGGNet/VOC0712Plus/SSD_300x300_ft/deploy.prototxt"
weights_path_prefix: "models/VGGNet/VOC0712Plus/SSD_300x300_ft/VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.caffemodel"
delete_output_param: True
tar_size: 97789219
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,300,300] --input data --mean_values data[104.0,117.0,123.0] --output detection_out --input_model <ssd300.caffemodel> --input_proto <ssd300.prototxt>"
framework: caffe
license: https://github.com/weiliu89/caffe/blob/ssd/LICENSE
- name: "inception-resnet-v2"
description: "Inception-ResNet V2 architecture (https://arxiv.org/pdf/1602.07261.pdf)"
model: https://drive.google.com/drive/folders/0B9mkjlmP0d7zc3A4NWlQQzdoM28
model_hash: 8d53ee1f4112716e3e53d65ddc1936eeb6ca8f26d49cdb1129aba3bb78bdc209
weights: https://drive.google.com/drive/folders/0B9mkjlmP0d7zc3A4NWlQQzdoM28
weights_hash: 1027d403ba8bf6211ca0d25f4b9ec6d4357d1407a812835b741004d5ad308b92
output: "classification/inception-resnet/v2/caffe"
weights_google_drive_id: 0B9mkjlmP0d7zNmY5eXZhLUhzQVE
model_google_drive_id: 0B9mkjlmP0d7zUXhsMmI4cWR6MmM
weights_size: 223511172
model_size: 192362
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,299,299] --input data --output prob --input_model <inception-resnet-v2.caffemodel> --input_proto <inception-resnet-v2.prototxt>"
framework: caffe
license: https://github.com/soeaver/caffe-model/blob/master/LICENSE
- name: "dilation"
description: "Multi-Scale Context Aggregation by Dilated Convolutions (https://arxiv.org/pdf/1511.07122.pdf)"
model: https://raw.githubusercontent.com/fyu/dilation/0f105742e8c2202af12feb54374781ba55c3e112/models/dilation10_cityscapes_deploy.prototxt
weights: http://dl.yf.io/dilation/models/dilation10_cityscapes.caffemodel
model_hash: 7b1adfc35901b5626504b44fbf3567bcf81926b9482b031925012e8fbef8f219
weights_hash: ec6cec2bfa0f1b199f11e73f0e030ae29326bc5648f3ddf9127feb3367b68b92
output: "semantic_segmentation/dilation/cityscapes/caffe"
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,1396,1396] --input data --mean_values data[72.39,82.91,73.16] --output prob --input_model <dilation.caffemodel> --input_proto <dilation.prototxt>"
framework: caffe
license: https://github.com/fyu/dilation/blob/master/LICENSE
- name: "googlenet-v1"
description: "GoogleNet v1 (Inception v1) (https://arxiv.org/pdf/1409.4842.pdf)"
model: https://raw.githubusercontent.com/BVLC/caffe/88c96189bcbf3853b93e2b65c7b5e4948f9d5f67/models/bvlc_googlenet/deploy.prototxt
model_hash: fa36cd4112a3240a29600a5aa35333f31bec33daa1a6085b0af308a9f50f0c50
weights: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
weights_hash: 6f7101e3a2183738a7125a0c5021ba82a1feb4228c5ca0924d991b6daf6f6fad
output: "classification/googlenet/v1/caffe"
old_dims: [10,3,224,224]
new_dims: [1,3,224,224]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[104.0,117.0,123.0] --output prob --input_model <googlenet-v1.caffemodel> --input_proto <googlenet-v1.prototxt>"
framework: caffe
license: https://github.com/BVLC/caffe/blob/master/LICENSE
- name: "googlenet-v2"
description: "GoogleNet v2 (Inception v2) (https://arxiv.org/pdf/1502.03167.pdf)"
model: https://raw.githubusercontent.com/lim0606/caffe-googlenet-bn/d19caf526b7d8cad873ff91ba4cea602eadd58b3/deploy.prototxt
model_hash: 18c77f01cca83c4cee973bfd8739968210dd6a1e5af1b857faaec230c5aa7d5a
weights: https://github.com/lim0606/caffe-googlenet-bn/raw/d19caf526b7d8cad873ff91ba4cea602eadd58b3/snapshots/googlenet_bn_stepsize_6400_iter_1200000.caffemodel
weights_hash: c6581a8e60e5ae8962183ff019986fc9dfacc5e5bd5247d76a0ff7fae8e0409a
output: "classification/googlenet/v2/caffe"
old_dims: [10,3,224,224]
new_dims: [1,3,224,224]
layers_to_layer: True
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --mean_values data[104.0,117.0,123.0] --output prob --input_model <googlenet-v2.caffemodel> --input_proto <googlenet-v2.prototxt>"
framework: caffe
license: https://github.com/lim0606/caffe-googlenet-bn/blob/master/README.md
- name: "googlenet-v4"
description: "GoogleNet v4 (Inception v4)"
model: https://drive.google.com/drive/folders/0B9mkjlmP0d7zUEJ3aEJ2b3J0RFU
model_hash: 2213d8729839cd015c4b0335d91da1cf4a034627fadedcdb5bf8ac347c4896d9
weights: https://drive.google.com/drive/folders/0B9mkjlmP0d7zUEJ3aEJ2b3J0RFU
weights_hash: 6ff248c1215a9fc14ac7ccd44b03da35e41e50bde054ba201bd9c737522996c3
output: "classification/googlenet/v4/caffe"
weights_google_drive_id: 0B9mkjlmP0d7zeG1HREVMR2F3WmM
model_google_drive_id: 0B9mkjlmP0d7zWEJsRl9zeTQ1NzA
weights_size: 170777072
model_size: 86534
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,299,299] --input data --mean_values data[128.0,128.0,128.0] --scale_values data[128.0] --output prob --input_model <googlenet-v4.caffemodel> --input_proto <googlenet-v4.prototxt>"
framework: caffe
license: https://github.com/soeaver/caffe-model/blob/master/LICENSE
- name: "alexnet"
description: "AlexNet (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)"
model: https://raw.githubusercontent.com/BVLC/caffe/88c96189bcbf3853b93e2b65c7b5e4948f9d5f67/models/bvlc_alexnet/deploy.prototxt
model_hash: b564ba76416b4ac1e062c6621cbd80d9c19304ba152d9d791c75870f3182c4fa
weights: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
weights_hash: 4bff209a9837298157915ef50a4831a59636a6ca1a6b8ebacd990c3a5f3053e0
output: "classification/alexnet/caffe"
old_dims: [10,3,227,227]
new_dims: [1,3,227,227]
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,227,227] --input data --mean_values data[104.0,117.0,123.0] --output prob --input_model <alexnet.caffemodel> --input_proto <alexnet.prototxt>"
framework: caffe
license: https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/readme.md
- name: "ssd_mobilenet_v2_coco"
description: "MobileNetV2 object detection architecture (https://arxiv.org/pdf/1801.04381.pdf) pre-trained on the COCO dataset"
model_hash: 2a8d8a89d695842e60d8c6d144181100555563e21acf2fa1e8f561fec5c3c6ad
tar: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz
output: "object_detection/common/ssd_mobilenet_v2_coco/tf"
model_path_prefix: "ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb"
model_optimizer_args: "--framework tf --data_type FP32 --reverse_input_channels --input_shape [1,300,300,3] --input image_tensor --tensorflow_use_custom_operations_config ./extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config ./pipeline.config --output detection_classes,detection_scores,detection_boxes,num_detections --input_model <ssd_mobilenet_v2_coco.pb>"
framework: tf
license: https://github.com/tensorflow/models/blob/master/LICENSE
- name: "resnet-50"
description: "ResNet-50 (https://arxiv.org/pdf/1512.03385.pdf)"
model: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117891&authkey=AAFW2-FVoxeVRck
model_hash: 2f8fb64f68c6bcda94eb2640f80aed94efb91664122e72a6b7587012cc57dedc
weights: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117895&authkey=AAFW2-FVoxeVRck
weights_hash: 44ee2b08816cede2b7aaa047888df07dcab52f73399aa1c8bef05a17bfdd4888
output: "classification/resnet/v1/50/caffe/"
weights_size: 102462397
model_size: 32500
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --output prob --input_model <resnet-50.caffemodel> --input_proto <resnet-50.prototxt>"
framework: caffe
license: https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE
- name: "resnet-101"
description: "ResNet-101 (https://arxiv.org/pdf/1512.03385.pdf)"
model: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117892&authkey=AAFW2-FVoxeVRck
model_hash: 5df4375748076544e6cc4bfde7885e633312b5ed529d65cc2ff6573a819ea972
weights: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117896&authkey=AAFW2-FVoxeVRck
weights_hash: 2847d93346d7928ec1f257f49f60ea53e9075c15265c494469610e67ffb7f2e2
output: "classification/resnet/v1/101/caffe/"
weights_size: 178662602
model_size: 65439
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --output prob --input_model <resnet-101.caffemodel> --input_proto <resnet-101.prototxt>"
framework: caffe
license: https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE
- name: "resnet-152"
description: "ResNet-152 (https://arxiv.org/pdf/1512.03385.pdf)"
model: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117893&authkey=AAFW2-FVoxeVRck
model_hash: 9e0fc0df6ac038048a87c63d56fd534471b6b4f39d55651beaf201763e028489
weights: https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117897&authkey=AAFW2-FVoxeVRck
weights_hash: 6253c4c4132c0b25c112b166629aa57dcaeec044a4c68ac9f003b6c801329d55
output: "classification/resnet/v1/152/caffe/"
weights_size: 241444171
model_size: 98034
model_optimizer_args: "--framework caffe --data_type FP32 --input_shape [1,3,224,224] --input data --output prob --input_model <resnet-152.caffemodel> --input_proto <resnet-152.prototxt>"
framework: caffe
license: https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE
- name: "googlenet-v3"
description: "GoogleNet v3 (Inception v3) (https://arxiv.org/pdf/1512.00567.pdf)"
model_hash: f5ce67052687aba5bc35250b51e8d0f127d3fd140eac67b47c07d012d20d55a3
tar: https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
output: "classification/googlenet/v3/tf/"
model_path_prefix: "inception_v3_2016_08_28_frozen.pb"
model_optimizer_args: "--framework tf --data_type FP32 --reverse_input_channels --input_shape [1,299,299,3] --input input --scale_values input[254.99997577500233] --output InceptionV3/Predictions/Softmax --input_model <googlenet-v3.pb>"
framework: tf
license: https://github.com/tensorflow/models/blob/master/LICENSE
- name: "age-gender-recognition-retail-0013"
description: "Age & gender classification."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml
model_hash: f02b83c9844a5fb0b4add8cd55125cb55065c0779c3ccb58252271621d345246
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.bin
weights_hash: f4517b6c53e15611e19f2bfe220bfd20d7e72eed39444747fea12cf6a431b97e
output: "Retail/object_attributes/age_gender/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "age-gender-recognition-retail-0013-fp16"
description: "Age & gender classification."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml
model_hash: d16a904fbdd9d7bd7543ebf4e0c8bd326e5f6003bbad7591b73c394913bbce4f
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.bin
weights_hash: 3586df5340e9fcd73ba0e2d802631bd9e027179490635c03b273d33d582e2b58
output: "Retail/object_attributes/age_gender/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "emotions-recognition-retail-0003"
description: "Recognizes 5 emotions for a face."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/emotions-recognition-retail-0003/FP32/emotions-recognition-retail-0003.xml
model_hash: 8a59f54404e97507905d0a6d0ac3735215dcf3b0aec95305513e345f3a808682
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/emotions-recognition-retail-0003/FP32/emotions-recognition-retail-0003.bin
weights_hash: bcb9b1a910fa3cd18a638bb1dbb0597c4ef7a080d1b83008c8e8c2c3c42b99dd
output: "Retail/object_attributes/emotions_recognition/0003/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "emotions-recognition-retail-0003-fp16"
description: "Recognizes 5 emotions for a face."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/emotions-recognition-retail-0003/FP16/emotions-recognition-retail-0003.xml
model_hash: 1b19c89fe62ed8f1adc8c898fbe2c46affe960f711c86c9b9ff1eb972d355bc6
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/emotions-recognition-retail-0003/FP16/emotions-recognition-retail-0003.bin
weights_hash: e62fb4b819b3b3ad8aafcd308d4353db2f164a1a31d78de6cf5970837aeb6f7b
output: "Retail/object_attributes/emotions_recognition/0003/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-detection-adas-0001"
description: "Face Detection (MobileNet with reduced channels + SSD with weights sharing)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-adas-0001/FP32/face-detection-adas-0001.xml
model_hash: 7691ed4462a92b18ea69c2a00852149de0fe78e5b1680adbeaeed82438914bb9
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-adas-0001/FP32/face-detection-adas-0001.bin
weights_hash: b2681ed6f23fb4337d76bade4a24517ae22bce8320eb82c55d8df8f42d7d299e
output: "Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-detection-adas-0001-fp16"
description: "Face Detection (MobileNet with reduced channels + SSD with weights sharing)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.xml
model_hash: b3e914eb453e56ec14acf1832f281286f362a754e432d443979a5b26eef3d1f3
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.bin
weights_hash: 7f5e1aea0f4397700e00d90c2f94c5f6cc1ccce2c984aa487548768b65da26aa
output: "Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-detection-retail-0004"
description: "Face Detection (SqNet1.0modif+single scale) without BatchNormalization trained with negatives."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-retail-0004/FP32/face-detection-retail-0004.xml
model_hash: a1a98b1b5f2fdbde90e02e436e716b8b7740c16ee1e04707e190ac8295f76072
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-retail-0004/FP32/face-detection-retail-0004.bin
weights_hash: dbe22678ee9b9402a5010fa507dcbe83739d891f3538235039b2c7f5d0f90d75
output: "Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-detection-retail-0004-fp16"
description: "Face Detection (SqNet1.0modif+single scale) without BatchNormalization trained with negatives."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-retail-0004/FP16/face-detection-retail-0004.xml
model_hash: 5894ac4b45cd656e44283813f0a474d864df32e6493260faae2c114e7807f87d
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-detection-retail-0004/FP16/face-detection-retail-0004.bin
weights_hash: 88cc5a150564bc4d3c1616d2d7a18f171fc7e00b60ac3551f908527642d14b4e
output: "Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-person-detection-retail-0002"
description: "Simultaneous detection of Pedestrians and Faces (RMNet with lrelu + SSSSD)."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-person-detection-retail-0002/FP32/face-person-detection-retail-0002.xml
model_hash: 44363a97f84eefe467b3f48c08bfdabde90aa12367d2a13b882db8b331c71658
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-person-detection-retail-0002/FP32/face-person-detection-retail-0002.bin
weights_hash: a1afb3fb46122289bd8ea64fa5f78569ed312c00899f24d01a42ae1f76f0811d
output: "Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-person-detection-retail-0002-fp16"
description: "Simultaneous detection of Pedestrians and Faces (RMNet with lrelu + SSSSD)."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-person-detection-retail-0002/FP16/face-person-detection-retail-0002.xml
model_hash: 85e3f1e2e35451a349681da620baf8d8ea5ca3d51fb9c8d0f8467c57bc8184c5
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-person-detection-retail-0002/FP16/face-person-detection-retail-0002.bin
weights_hash: f072f713b4033039eb1450318bef305d0cb74164d9019f1594161d5918802a85
output: "Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-reidentification-retail-0001"
description: "Single embedding-based face verification model"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-reidentification-retail-0001/FP32/face-reidentification-retail-0001.xml
model_hash: 5f68647b7d94b884521f9cbc282f740881cffcc13c0038aea81bcd5a358b33b0
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-reidentification-retail-0001/FP32/face-reidentification-retail-0001.bin
weights_hash: 16b1d4b290643278f8819e0e0938427e36549f9ba9a68ba0720d918514ec9db9
output: "Retail/object_reidentification/face/rmnet_based/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "face-reidentification-retail-0001-fp16"
description: "Single embedding-based face verification model"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-reidentification-retail-0001/FP16/face-reidentification-retail-0001.xml
model_hash: 07d27e7b6a6146619ba3f9c20d527ec419166585f383443dedaf0d1110cb9074
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/face-reidentification-retail-0001/FP16/face-reidentification-retail-0001.bin
weights_hash: c0d0254eaeef5f9d7adb264b49b5790781a4edea83cc5db844ad3558efe7e7c2
output: "Retail/object_reidentification/face/rmnet_based/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "head-pose-estimation-adas-0001"
description: "Vanilla CNN trained from scratch yaw + pitch + roll + landmarks"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml
model_hash: ee31ddb543d38d40ba8291f9ab4105e34436128a8dc639d0d3c4d580f1786864
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.bin
weights_hash: ed63b49ef631b61e38f04e24390b6e7ee260ed783c585f76a0f56d6aa062f336
output: "Transportation/object_attributes/headpose/vanilla_cnn/dldt"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "head-pose-estimation-adas-0001-fp16"
description: "Vanilla CNN trained from scratch yaw + pitch + roll + landmarks"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.xml
model_hash: d60b8e225efe9ec9000e21d65db62c584fcd54fa9d78770800d943656bd1788f
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.bin
weights_hash: 535a6af806999e22cca5e4071e55841a694a6b60370a6f8fb3b9d0cda5f81c41
output: "Transportation/object_attributes/headpose/vanilla_cnn/dldt"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "landmarks-regression-retail-0001"
description: "Landmark's detection."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/landmarks-regression-retail-0001/FP32/landmarks-regression-retail-0001.xml
model_hash: f9e9af83fb8439d2a6a682f98842d26a743055d2b4ec3c3fab536fada0881359
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/landmarks-regression-retail-0001/FP32/landmarks-regression-retail-0001.bin
weights_hash: c9aa10a8648d6902055bb00346a9c2f1c6319c62b0ce14688769284ef03847a7
output: "Retail/object_attributes/landmarks_regression/0001/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "landmarks-regression-retail-0001-fp16"
description: "Landmark's detection."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/landmarks-regression-retail-0001/FP16/landmarks-regression-retail-0001.xml
model_hash: 6a71d4b660b368c184518a534c8d0a0569465ca944c5c96d4db031eb36371826
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/landmarks-regression-retail-0001/FP16/landmarks-regression-retail-0001.bin
weights_hash: 8a1e5560f95aa9664c2254708a52dc4ed98766c6f8c565fb1dbbc127856f946d
output: "Retail/object_attributes/landmarks_regression/0001/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "license-plate-recognition-barrier-0001"
description: "Chinese license plate recognition"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
model_hash: 26cc3146c02b5043ce46e67080b5d3647b1411611cefb095c13d78cd34dea893
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.bin
weights_hash: 60f6ad18d05373e3a82030aca3b37606afa39f6a5d72f636bf91f15cd020b885
output: "Security/optical_character_recognition/license_plate/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "license-plate-recognition-barrier-0001-fp16"
description: "Chinese license plate recognition"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml
model_hash: 6968338d2af2bd9889df770b09b81b85b24b97ff18a5e6adfcfe26f9d3cf6f7e
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.bin
weights_hash: 4982af6c19dca69dbb95167e2ba78026726c8e1b6554d148e94c0fbee8b83d27
output: "Security/optical_character_recognition/license_plate/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "pedestrian-and-vehicle-detector-adas-0001"
description: "Pedestrian and Vehicle detector based on ssd + mobilenet with reduced channels number."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml
model_hash: 1519004c4fa66231c701a715171659a0581ddca067744969286236e74a7f7f98
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.bin
weights_hash: 4d580c8de7e51be7308927ffa6d1c79a64ca44ba47dce4742a6e9da3ac2fb675
output: "Transportation/object_detection/pedestrian-and-vehicle/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "pedestrian-and-vehicle-detector-adas-0001-fp16"
description: "Pedestrian and Vehicle detector based on ssd + mobilenet with reduced channels number."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-and-vehicle-detector-adas-0001/FP16/pedestrian-and-vehicle-detector-adas-0001.xml
model_hash: ee485b49f43d5e876df8143be7c55cb1b39fa3a8b272b0efab5e5eac18efab6c
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-and-vehicle-detector-adas-0001/FP16/pedestrian-and-vehicle-detector-adas-0001.bin
weights_hash: 743205b4d2f9d8650dfb7d89023154ce2ddcc803fc3a8fece480d69e1150bbf2
output: "Transportation/object_detection/pedestrian-and-vehicle/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "pedestrian-detection-adas-0002"
description: "Pedestrian detector based on ssd + mobilenet with reduced channels number."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-detection-adas-0002/FP32/pedestrian-detection-adas-0002.xml
model_hash: 8efe2e3e19f2d55848ad3e8b5b3dad4dda08228c55ad7652e1fa0337bb168fc7
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-detection-adas-0002/FP32/pedestrian-detection-adas-0002.bin
weights_hash: 74c8cb42a37448e808f16e1e0da9994c593271b5148b8b21e09d358d9d17eac1
output: "Transportation/object_detection/pedestrian/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "pedestrian-detection-adas-0002-fp16"
description: "Pedestrian detector based on ssd + mobilenet with reduced channels number."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-detection-adas-0002/FP16/pedestrian-detection-adas-0002.xml
model_hash: 585d3be4feec05b359159d086863c65747e309284cd4857aa6a88d90eabb688f
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/pedestrian-detection-adas-0002/FP16/pedestrian-detection-adas-0002.bin
weights_hash: 3a993502380b550b2dd01b8f54727b1b2531261e72955e1b422a17ff3c7d389b
output: "Transportation/object_detection/pedestrian/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-attributes-recognition-crossroad-0031"
description: "Pedestrian attributes recognition based on a PVANet with hyperfeatures backbone + classification head"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-attributes-recognition-crossroad-0031/FP32/person-attributes-recognition-crossroad-0031.xml
model_hash: adc67ba976b0e918e8bb5ceb21d522b8b13ca1631d873c4db77451b1a4e5c8d3
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-attributes-recognition-crossroad-0031/FP32/person-attributes-recognition-crossroad-0031.bin
weights_hash: e6c80e600208db00f74127d4071f255e4a6aa1c488a31985fd45d16a85f62a2d
output: "Security/object_attributes/pedestrian/person-attributes-recognition-crossroad-0031/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-attributes-recognition-crossroad-0031-fp16"
description: "Pedestrian attributes recognition based on a PVANet with hyperfeatures backbone + classification head"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-attributes-recognition-crossroad-0031/FP16/person-attributes-recognition-crossroad-0031.xml
model_hash: 45b92c1a57f7a70a431eec5b1cb029f1af037445166f3ed6c9fb25236b294134
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-attributes-recognition-crossroad-0031/FP16/person-attributes-recognition-crossroad-0031.bin
weights_hash: 2013a2583215abc0f9536d5b7715bcf17c1253caabc2bc50a2740bb94d5295f6
output: "Security/object_attributes/pedestrian/person-attributes-recognition-crossroad-0031/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-action-recognition-0001"
description: "Action detection (SSD-based) model."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-action-recognition-0001/FP32/person-detection-action-recognition-0001.xml
model_hash: 1ac6273da61a7e03a4c5ecd15a9b65eeeb3eeb49a8e16062041c9606cbb95dae
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-action-recognition-0001/FP32/person-detection-action-recognition-0001.bin
weights_hash: e5be630245692cf3c09f9d8c20c15fd14f6e8dc9a506ab16ec67e33c4e20df06
output: "Retail/action_detection/pedestrian/rmnet_ssd/0023/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-action-recognition-0001-fp16"
description: "Action detection (SSD-based) model."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-action-recognition-0001/FP16/person-detection-action-recognition-0001.xml
model_hash: 34506bdf22235ab49f688e2497e7ec273b97d25d2c474e2f9ae84c993c03b623
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-action-recognition-0001/FP16/person-detection-action-recognition-0001.bin
weights_hash: b5c839302c8f73da950da109d06db2f529edb63dc505b3e76ddd5691d045bbd1
output: "Retail/action_detection/pedestrian/rmnet_ssd/0023/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-retail-0001"
description: "Person detection (HyperNet+RFCN)."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0001/FP32/person-detection-retail-0001.xml
model_hash: 66897101aaa6493775c86a6f51308b87d17c4457f96b3aa8e685806992aa601f
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0001/FP32/person-detection-retail-0001.bin
weights_hash: e86c8bf1d6a431ea9445681cfd44180827c69566a736ce5d1ae1678eb9ee9ff8
output: "Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-retail-0001-fp16"
description: "Person detection (HyperNet+RFCN)."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0001/FP16/person-detection-retail-0001.xml
model_hash: 698700008dbd398ceffc64ea9da6c1d4e468322a04072088f9eb9ff0700116ad
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0001/FP16/person-detection-retail-0001.bin
weights_hash: 7bc965628462c7ffe964eb3e52f426b81d880e3a5ce5cdc1281e1ab91b819d79
output: "Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-retail-0013"
description: "Pedestrian detection (RMNet with lrelu + SSD)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
model_hash: 1f4996823b9cf53d491b88fa664741ef8eb82d51918039709b1d206e6a0fb3e2
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0013/FP32/person-detection-retail-0013.bin
weights_hash: 7a1f5d5ea170366974f192b0bac4a2c64a78bdb1e608f5859556d20a1f45d0aa
output: "Retail/object_detection/pedestrian/rmnet_ssd/0013/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-detection-retail-0013-fp16"
description: "Pedestrian detection (RMNet with lrelu + SSD)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0013/FP16/person-detection-retail-0013.xml
model_hash: d10bfc66a607fe1473549d6e6b71eb93a0b246ecea032b1d0c00b9b8effa4d38
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-detection-retail-0013/FP16/person-detection-retail-0013.bin
weights_hash: fb8f027f2b1afec4c96e8883398b8f93e1d704838b463a9d0c2cfbb6a5448aed
output: "Retail/object_detection/pedestrian/rmnet_ssd/0013/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0031"
description: "Single embedding-based person reidentification model (fastest person ReID model)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0031/FP32/person-reidentification-retail-0031.xml
model_hash: 041561571fb4c3ea08c01bcd168bda59b0a1f4903e93119e5c3b139755ec210a
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0031/FP32/person-reidentification-retail-0031.bin
weights_hash: 8b9d349d330909815d2e75b57654c4cd6c27eb37c87d3280dfeaae03d166a4f4
output: "Retail/object_reidentification/pedestrian/rmnet_based/0031/dldt"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0031-fp16"
description: "Single embedding-based person reidentification model (fastest person ReID model)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0031/FP16/person-reidentification-retail-0031.xml
model_hash: a798070bb6b7f3e5230cb1e8beeaf8995a8cc835ef3e063cf61832eb4335fe95
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0031/FP16/person-reidentification-retail-0031.bin
weights_hash: 833c136bcce82bcba6de6289fc21ee24f24d4597e52c41717c2602ee275b2f7d
output: "Retail/object_reidentification/pedestrian/rmnet_based/0031/dldt"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0076"
description: "Single embedding-based person reidentification model (most accurate person ReID model)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0076/FP32/person-reidentification-retail-0076.xml
model_hash: 7996001a9fd604a0608d4ff059418eb8f6e8354037f36ce597910e3701d6b575
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0076/FP32/person-reidentification-retail-0076.bin
weights_hash: acd718a8e6bd0c9e52605ecfe3f5a7ba87280976315bccc94e9302bbe520d898
output: "Retail/object_reidentification/pedestrian/rmnet_based/0076/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0076-fp16"
description: "Single embedding-based person reidentification model (most accurate person ReID model)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0076/FP16/person-reidentification-retail-0076.xml
model_hash: c5211212da009799a0dd0a4410cd4a6e02d102d24459ab728b3922f4e161fc68
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0076/FP16/person-reidentification-retail-0076.bin
weights_hash: c8a127cb2b8ff5f26b1d248de5a07b79c916103ee39bec0825fb990e4b043fd6
output: "Retail/object_reidentification/pedestrian/rmnet_based/0076/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0079"
description: "Single embedding-based person reidentification model (the model is trade-off between performance and accuracy)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0079/FP32/person-reidentification-retail-0079.xml
model_hash: 2c9f2ef39e6b43b8f7659b2c621f9fb242c6cbd819c7e70e9f13c8ebbd30da2c
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0079/FP32/person-reidentification-retail-0079.bin
weights_hash: e341d80b568d939dc121d2c4785d1f8bc90df477cd1754d0b286b2e24b7b33e4
output: "Retail/object_reidentification/pedestrian/rmnet_based/0079/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-reidentification-retail-0079-fp16"
description: "Single embedding-based person reidentification model (the model is trade-off between performance and accuracy)"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0079/FP16/person-reidentification-retail-0079.xml
model_hash: dd5ca648b772295877f5a139791e7ea388ada808db2bbcdef776ebc7c769e278
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-reidentification-retail-0079/FP16/person-reidentification-retail-0079.bin
weights_hash: 95c426b1d7e8f06f417172f5b1058bd906c769c8c10b3846f8b3adc826390d93
output: "Retail/object_reidentification/pedestrian/rmnet_based/0079/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-vehicle-bike-detection-crossroad-0078"
description: "Multiclass (person, vehicle, non-vehicle) detector based on SSD detection architecture, RMNet backbone and learnable image downscale block"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml
model_hash: 7c41292171295af0174a1f76a663a7842068f62cda3fff6e994b0206377a0fc6
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.bin
weights_hash: 34cb7d20e3eaf5dba8a39c93f8c0031b4b7779b059cff933d8137072b5927bff
output: "Security/object_detection/crossroad/0078/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "person-vehicle-bike-detection-crossroad-0078-fp16"
description: "Multiclass (person, vehicle, non-vehicle) detector based on SSD detection architecture, RMNet backbone and learnable image downscale block"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-vehicle-bike-detection-crossroad-0078/FP16/person-vehicle-bike-detection-crossroad-0078.xml
model_hash: 0dd85b85eda4b61d77208371ce18a85ff8f2da3085368b759c52a0edba9dcf89
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/person-vehicle-bike-detection-crossroad-0078/FP16/person-vehicle-bike-detection-crossroad-0078.bin
weights_hash: 726f5c8c520fd05ce40917c667962e4c63d79343fea215d2a0acd3600e8ae7cb
output: "Security/object_detection/crossroad/0078/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "road-segmentation-adas-0001"
description: "Multiclass (BG, road, curbs, marks) segmentation based on ENET, using depthwise convolutions and without ELU operations and without concatenation"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.xml
model_hash: 3149e3abcb57f151cb80a3946ddbf4075d714637c996b3a16fc7565f92e85f95
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.bin
weights_hash: e4ec8fa66deb6904b5b6faa109fa699098cc1f947bf5216c6e31595ec397c569
output: "Transportation/segmentation/curbs/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "road-segmentation-adas-0001-fp16"
description: "Multiclass (BG, road, curbs, marks) segmentation based on ENET, using depthwise convolutions and without ELU operations and without concatenation"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml
model_hash: c19e876ffe57bde60f8f85ab6d5acba2ff2e996b007cb6e6bfbdb32ca6684104
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.bin
weights_hash: 292b8c8789d66f102ffb9b87104b39488bfa55293ef97e7d6a50439762cdd884
output: "Transportation/segmentation/curbs/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "semantic-segmentation-adas-0001"
description: "Multiclass (road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle, bicycle, ego-vehicle) segmentation based on ICNet"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/semantic-segmentation-adas-0001/FP32/semantic-segmentation-adas-0001.xml
model_hash: 757d0e4c732419cca166723e5865ae6375eaaebef3387915090ddd5d2dbe278b
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/semantic-segmentation-adas-0001/FP32/semantic-segmentation-adas-0001.bin
weights_hash: 5a616b105fa42f574e4d23cfc227c10b8bdcc0ba0e66864d7aa4b8a269b7cc76
output: "Transportation/segmentation/semantic_segmentation/icnet_icv/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "semantic-segmentation-adas-0001-fp16"
description: "Multiclass (road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle, bicycle, ego-vehicle) segmentation based on ICNet"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/semantic-segmentation-adas-0001/FP16/semantic-segmentation-adas-0001.xml
model_hash: 24f3ea0eb4033b7d519dd54f9eff9057c29ed0691fb601d8c2c127ce5aa03132
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/semantic-segmentation-adas-0001/FP16/semantic-segmentation-adas-0001.bin
weights_hash: 8c39dcb0d3569003264165eda163833eef2d0803121147ab06f46697e4f8b4c4
output: "Transportation/segmentation/semantic_segmentation/icnet_icv/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-attributes-recognition-barrier-0039"
description: "Vehicle attributes recognition with modified ResNet10 backbone"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml
model_hash: 4cdd2558c1b869ef1573208d0e2003bdac6c66290a1c150dd37ccc30458649ca
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.bin
weights_hash: e07af818a25278b575ea6a3bd55d3dc39d136d65218ac175e7523665bd3e3b0e
output: "Security/object_attributes/vehicle/resnet10_update_1/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-attributes-recognition-barrier-0039-fp16"
description: "Vehicle attributes recognition with modified ResNet10 backbone"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
model_hash: 289afa9e0172d520193b22bfd5b950df400aed22225752e37b16873ae756d5e7
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.bin
weights_hash: be8b61e337a23ffa57d1742f92d2317560303652f330fef0cbe918161bfcaf50
output: "Security/object_attributes/vehicle/resnet10_update_1/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-detection-adas-0002"
description: "Vehicle detector based on SSD + MobileNet with reduced number of channels and depthwise head."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-detection-adas-0002/FP32/vehicle-detection-adas-0002.xml
model_hash: 86f819f4656de9e4c8cd936decac5217ad6ade67ac9b20afbdf1cd717cded171
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-detection-adas-0002/FP32/vehicle-detection-adas-0002.bin
weights_hash: 510aba5c6d68456d3718c4192b28e58df257320519c752d8d9f8f201fac29120
output: "Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-detection-adas-0002-fp16"
description: "Vehicle detector based on SSD + MobileNet with reduced number of channels and depthwise head."
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-detection-adas-0002/FP16/vehicle-detection-adas-0002.xml
model_hash: 5727bcf11e330340e7474c0eefb6712c3968bfd0c9e3eecb5b83b545e18e4605
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-detection-adas-0002/FP16/vehicle-detection-adas-0002.bin
weights_hash: f892f90f87e11424a840850deab0ab4bd0d1b1d89a290f399c532a3316752cb5
output: "Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-license-plate-detection-barrier-0106"
description: "Multiclass (vehicle, license plates) detector based on MobileNetV2+SSD"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml
model_hash: 5a368411c4f960096ca67000e5f323319f4085c9dc6676143474440a291ba42e
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.bin
weights_hash: 16de87bdb2158504c84edf632b01c6791d2d4b15e277722e566fe45e16646ab5
output: "Security/object_detection/barrier/0106/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
- name: "vehicle-license-plate-detection-barrier-0106-fp16"
description: "Multiclass (vehicle, license plates) detector based on MobileNetV2+SSD"
model: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml
model_hash: 9b05e17e964e08499a197c4dc34cc84e5fc2dab65ff215ae95e4306cee36d97f
weights: https://download.01.org/openvinotoolkit/2018_R3/open_model_zoo/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin
weights_hash: ea529b73b2104ff63757f767d148d059230d601c4e27ae9d8d395dc983621e14
output: "Security/object_detection/barrier/0106/dldt/"
framework: dldt
license: https://github.com/opencv/open_model_zoo/blob/2018/LICENSE
Public Topologies Downloader
The script is designed to download popular public deep learning topologies and prepare models for the Model Optimizer tool.
Prerequisites
- Install python3 (version 3.5.2 or higher)
- Install yaml and requests modules with the command
sudo -E pip3 install pyyaml requests
Usage
-
Run the script with
-hkey to see the help message:./downloader.py -h usage: downloader.py [-h] [-c CONFIG] [--name NAME] [--print_all] [-o OUTPUT_DIR] optional arguments: -h, --help show this help message and exit -c CONFIG, --config CONFIG path to YML configuration file --name NAME name of topology for downloading --print_all print all available topologies -o OUTPUT_DIR, --output_dir OUTPUT_DIR path where to save topologies list_topologies.yml - default configuration file -
Run the script with the default configuration file:
./downloader.pyor with a custom configuration file:
./downloader.py -c <path_to_configuration_file> -
Run the script with the
--print_alloption to see the available topologies:./downloader.py --print_all densenet-121 densenet-161 densenet-169 densenet-201 squeezenet1.0 squeezenet1.1 mtcnn-p mtcnn-r mtcnn-o mobilenet-ssd vgg19 vgg16 ssd512 ssd300 inception-resnet-v2 dilation googlenet-v1 googlenet-v2 googlenet-v4 alexnet ssd_mobilenet_v2_coco age-gender-recognition-retail-0013 age-gender-recognition-retail-0013-fp16 emotions-recognition-retail-0003 emotions-recognition-retail-0003-fp16 face-detection-adas-0001 face-detection-adas-0001-fp16 face-detection-retail-0004 face-detection-retail-0004-fp16 face-person-detection-retail-0002 face-person-detection-retail-0002-fp16 face-reidentification-retail-0001 face-reidentification-retail-0001-fp16 head-pose-estimation-adas-0001 head-pose-estimation-adas-0001-fp16 landmarks-regression-retail-0001 landmarks-regression-retail-0001-fp16 license-plate-recognition-barrier-0001 license-plate-recognition-barrier-0001-fp16 pedestrian-and-vehicle-detector-adas-0001 pedestrian-and-vehicle-detector-adas-0001-fp16 pedestrian-detection-adas-0002 pedestrian-detection-adas-0002-fp16 person-attributes-recognition-crossroad-0031 person-attributes-recognition-crossroad-0031-fp16 person-detection-action-recognition-0001 person-detection-action-recognition-0001-fp16 person-detection-retail-0001 person-detection-retail-0001-fp16 person-detection-retail-0013 person-detection-retail-0013-fp16 person-reidentification-retail-0031 person-reidentification-retail-0031-fp16 person-reidentification-retail-0076 person-reidentification-retail-0076-fp16 person-reidentification-retail-0079 person-reidentification-retail-0079-fp16 person-vehicle-bike-detection-crossroad-0078 person-vehicle-bike-detection-crossroad-0078-fp16 road-segmentation-adas-0001 road-segmentation-adas-0001-fp16 semantic-segmentation-adas-0001 semantic-segmentation-adas-0001-fp16 vehicle-attributes-recognition-barrier-0039 vehicle-attributes-recognition-barrier-0039-fp16 vehicle-detection-adas-0002 vehicle-detection-adas-0002-fp16 vehicle-license-plate-detection-barrier-0106 vehicle-license-plate-detection-barrier-0106-fp16 -
Download only one topology (mtcnn-p in the following code example):
./downloader.py --name mtcnn-p
Expected free space to download all the topologies with the default configuration file is around 3.5 GB.
Copyright © 2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Step 2: Convert the downloaded pre-trained model into IR files
cd ~/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe
# Ensure that the OpenVINO environment is initialized
source ~/intel/computer_vision_sdk/bin/setupvars.sh
source /opt/intel/computer_vision_sdk/bin/setupvars.sh
# Use model optimizer to convert googlenet.caffemodel to IR
mo.py --data_type FP16 --input_model googlenet-v2.caffemodel --input_proto googlenet-v2.prototxt
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader$ cd classification/googlenet/v2/caffe/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$ source /opt/intel/computer_vision_sdk/bin/setupvars.sh
[setupvars.sh] OpenVINO environment initialized
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$ mo.py --data_type FP16 --input_model googlenet-v2.caffemodel --input_proto googlenet-v2.prototxt
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.caffemodel
- Path for generated IR: /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/.
- IR output name: googlenet-v2
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP16
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: False
- Reverse input channels: False
Caffe specific parameters:
- Enable resnet optimization: True
- Path to the Input prototxt: /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.prototxt
- Path to CustomLayersMapping.xml: Default
- Path to a mean file: Not specified
- Offsets for a mean file: Not specified
Model Optimizer version: 1.4.292.6ef7232d
[ SUCCESS ] Generated IR model.
[ SUCCESS ] XML file: /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/./googlenet-v2.xml
[ SUCCESS ] BIN file: /opt/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_downloader/classification/googlenet/v2/caffe/./googlenet-v2.bin
[ SUCCESS ] Total execution time: 4.05 seconds.
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$ ll
total 84976
drwxrwxr-x 2 strong strong 4096 11月 28 16:38 ./
drwxrwxr-x 3 strong strong 4096 11月 28 11:29 ../
-rw-rw-r-- 1 strong strong 22370064 11月 28 16:38 googlenet-v2.bin
-rw-rw-r-- 1 strong strong 64445495 11月 28 11:49 googlenet-v2.caffemodel
-rw-rw-r-- 1 strong strong 28372 11月 28 16:38 googlenet-v2.mapping
-rw-rw-r-- 1 strong strong 60272 11月 28 11:59 googlenet-v2.prototxt
-rw-rw-r-- 1 strong strong 94600 11月 28 16:38 googlenet-v2.xml
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$
If the script ran fine, you should see googlenet-v2.bin, googlenet-v2.mapping and googlenet-v2.xml in model_downloader/classification/googlenet/v2/caffe folder.
Step 3: Deploy the converted IR model onto Intel NCS 2 using the toolkit’s IE API
cd ~/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples
# Download a test image from the internet
wget -N https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg
# Ensure that the OpenVINO environment is initialized
source ~/intel/computer_vision_sdk/bin/setupvars.sh
# Run an inference on this image using a built-in sample code
python3 classification_sample.py -m ~/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.xml -i Felis_catus-cat_on_snow.jpg -d MYRIAD
python3 classification_sample.py -m /opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.xml -i Felis_catus-cat_on_snow.jpg -d MYRIAD
If everything ran fine, you should see the below message in the terminal window. Class #173 corresponds to the ‘tabby cat’ class/category. Try downloading other images of tabby cat and rerunning the example.
Image Felis_catus-cat_on_snow.jpg
0.3881836 label #173
0.3193359 label #54
0.2410889 label #7
0.0361328 label #200
0.0037460 label #84
0.0025158 label #66
0.0021381 label #10
0.0016766 label #473
0.0013685 label #198
0.0007257 label #152
Congratulations! You have successfully installed and configured Intel Distribution of OpenVINO Toolkit to develop smart apps for Intel® Neural Compute Stick 2.
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe$ cd ../../../../../
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools$ ll
total 40
drwxrwxrwx 10 root root 4096 11月 20 09:17 ./
drwxr-xr-x 11 root root 4096 11月 20 09:17 ../
drwxrwxrwx 6 root root 4096 11月 20 09:17 computer_vision_algorithms/
drwxrwxrwx 2 root root 4096 11月 20 09:17 demo/
drwxrwxrwx 3 root root 4096 11月 20 09:17 documentation/
drwxrwxrwx 4 root root 4096 11月 20 09:17 extension_generator/
drwxrwxrwx 9 root root 4096 11月 20 09:17 inference_engine/
drwxrwxrwx 29 root root 4096 11月 20 09:17 intel_models/
drwxrwxrwx 8 root root 4096 11月 28 11:59 model_downloader/
drwxrwxrwx 6 root root 4096 11月 20 09:17 model_optimizer/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools$ cd inference_engine/samples/python_samples/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$ ll
total 128
drwxrwxrwx 5 root root 4096 11月 23 21:57 ./
drwxrwxrwx 31 root root 4096 11月 20 09:17 ../
drwxrwxrwx 8 root root 4096 11月 20 09:17 accuracy_checker/
-rwxrwxrwx 1 root root 5079 11月 20 09:17 affinity_setting_demo.py*
-rwxrwxrwx 1 root root 6294 11月 20 09:17 classification_sample_async.py*
-rwxrwxrwx 1 root root 6199 11月 20 09:17 classification_sample.py*
drwxrwxrwx 2 root root 4096 11月 20 09:17 cross_check_tool/
drwxrwxrwx 2 root root 4096 11月 20 09:17 greengrass_samples/
-rwxrwxrwx 1 root root 31675 11月 20 09:17 image_net_synset.txt*
-rwxrwxrwx 1 root root 8318 11月 23 21:57 object_detection_demo_ssd_async.py*
-rwxrwxrwx 1 root root 12897 11月 20 09:17 object_detection_demo_yolov3.py*
-rwxrwxrwx 1 root root 19 11月 20 09:17 requirements.txt*
-rwxrwxrwx 1 root root 6555 11月 20 09:17 segmentation_sample.py*
-rwxrwxrwx 1 root root 6687 11月 20 09:17 style_transfer_sample.py*
-rwxrwxrwx 1 root root 145 11月 20 09:17 voc_labels.txt*
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$ wget -N https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg
--2018-11-28 16:41:50-- https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg
Resolving upload.wikimedia.org (upload.wikimedia.org)... 198.35.26.112, 2620:0:863:ed1a::2:b
Connecting to upload.wikimedia.org (upload.wikimedia.org)|198.35.26.112|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2125399 (2.0M) [image/jpeg]
Saving to: ‘Felis_catus-cat_on_snow.jpg’
Felis_catus-cat_on_snow.jpg 100%[==============================================================================>] 2.03M 50.1KB/s in 30s
2018-11-28 16:42:22 (68.7 KB/s) - ‘Felis_catus-cat_on_snow.jpg’ saved [2125399/2125399]
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$ source /opt/intel/computer_vision_sdk/bin/setupvars.sh
[setupvars.sh] OpenVINO environment initialized
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$ python3 classification_sample.py -m /opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.xml -i Felis_catus-cat_on_snow.jpg -d MYRIAD
[ INFO ] Loading network files:
/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.xml
/opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image Felis_catus-cat_on_snow.jpg is resized from (2000, 3000) to (224, 224)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference (1 iterations)
[ INFO ] Average running time of one iteration: 25.846004486083984 ms
[ INFO ] Processing output blob
[ INFO ] Top 10 results:
Image Felis_catus-cat_on_snow.jpg
0.3801270 label #173
0.2961426 label #54
0.2783203 label #7
0.0329590 label #200
0.0032921 label #84
0.0021286 label #66
0.0018044 label #10
0.0016289 label #473
0.0009890 label #198
0.0006843 label #152
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples/python_samples$
the --labels option
OpenVINO doesn’t [yet] ship with labels file for all supported models, so I pulled the ILSVRC2012 sysnset_words.txt from http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz and renamed it as googlenet-v1.labels. Note that the ILSVRC2012 synset_words.txt won’t work for GoogLeNet V2 since V2 was trained on ILSVRC2015, you’d have to fetch the 2015 labels file when running GoogLeNet V2.
http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
python3 classification_sample.py -m /opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.xml -i Felis_catus-cat_on_snow.jpg -d MYRIAD --labels /opt/intel/computer_vision_sdk/deployment_tools/model_downloader/classification/googlenet/v2/caffe/googlenet-v2.labels