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Update list of OMZ models for stress tests (#9391)

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Ilya Sharikov 2022-01-10 13:50:03 +03:00 committed by GitHub
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4 changed files with 8 additions and 126 deletions
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48
tests/stress_tests/.automation/memcheck_tests/nightly_configs/desktop_references_config.xml
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@ -224,18 +224,6 @@
<model path="public/caffenet/FP32/caffenet.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2794235" vmpeak="3100619" vmrss="1080539" vmhwm="1441460" />
<model path="public/caffenet/FP32/caffenet.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1824440" vmpeak="1825226" vmrss="854365" vmhwm="854365" />
<model path="public/caffenet/FP32/caffenet.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="3104025" vmpeak="3196616" vmrss="1295985" vmhwm="1441315" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1222306" vmpeak="1222306" vmrss="302255" vmhwm="302255" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1982245" vmpeak="1999660" vmrss="625601" vmhwm="1096940" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1409361" vmpeak="1494558" vmrss="331500" vmhwm="331500" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2072449" vmpeak="2072449" vmrss="640104" vmhwm="1077991" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="1625124" vmpeak="1710061" vmrss="395668" vmhwm="395668" />
<model path="public/ctdet_coco_dlav0_384/FP16/ctdet_coco_dlav0_384.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2232453" vmpeak="2313906" vmrss="710814" vmhwm="1077315" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1139257" vmpeak="1139257" vmrss="218977" vmhwm="218977" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2159206" vmpeak="2180484" vmrss="829935" vmhwm="1202359" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="1326312" vmpeak="1411508" vmrss="248024" vmhwm="248024" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2245817" vmpeak="2331014" vmrss="825203" vmhwm="1206249" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1580420" vmpeak="1665617" vmrss="350963" vmhwm="351119" />
<model path="public/ctdet_coco_dlav0_384/FP32/ctdet_coco_dlav0_384.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2471809" vmpeak="2553262" vmrss="957522" vmhwm="1204455" />
<model path="public/ctdet_coco_dlav0_512/FP16/ctdet_coco_dlav0_512.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1240943" vmpeak="1240943" vmrss="304064" vmhwm="304064" />
<model path="public/ctdet_coco_dlav0_512/FP16/ctdet_coco_dlav0_512.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2038566" vmpeak="2087264" vmrss="680274" vmhwm="1115956" />
<model path="public/ctdet_coco_dlav0_512/FP16/ctdet_coco_dlav0_512.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1432990" vmpeak="1432990" vmrss="355217" vmhwm="355217" />
@ -272,18 +260,6 @@
<model path="public/densenet-121/FP32/densenet-121.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2131896" vmpeak="2217092" vmrss="850460" vmhwm="1368036" />
<model path="public/densenet-121/FP32/densenet-121.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1370922" vmpeak="1371692" vmrss="202976" vmhwm="203013" />
<model path="public/densenet-121/FP32/densenet-121.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2300828" vmpeak="2386025" vmrss="909090" vmhwm="1382570" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1354662" vmpeak="1354662" vmrss="291246" vmhwm="291246" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2062034" vmpeak="2107898" vmrss="924513" vmhwm="1447550" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1355411" vmpeak="1355411" vmrss="302666" vmhwm="302666" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2152191" vmpeak="2237388" vmrss="900109" vmhwm="1456780" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="1847050" vmpeak="1932247" vmrss="352216" vmhwm="352222" />
<model path="public/densenet-169/FP16/densenet-169.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2283476" vmpeak="2283476" vmrss="945224" vmhwm="1453857" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1207840" vmpeak="1207840" vmrss="203257" vmhwm="203257" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2155576" vmpeak="2208304" vmrss="1001166" vmhwm="1554956" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="1208584" vmpeak="1208584" vmrss="214489" vmhwm="214489" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2239140" vmpeak="2239140" vmrss="993444" vmhwm="1555356" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1811570" vmpeak="1896767" vmrss="316607" vmhwm="316607" />
<model path="public/densenet-169/FP32/densenet-169.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2435524" vmpeak="2520720" vmrss="1088292" vmhwm="1564035" />
<model path="public/efficientnet-b0/FP16/efficientnet-b0.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1019512" vmpeak="1019512" vmrss="108758" vmhwm="108758" />
<model path="public/efficientnet-b0/FP16/efficientnet-b0.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1955226" vmpeak="1968257" vmrss="672531" vmhwm="1135612" />
<model path="public/efficientnet-b0/FP16/efficientnet-b0.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1020682" vmpeak="1020682" vmrss="116802" vmhwm="116802" />
@ -486,18 +462,6 @@
<model path="public/se-inception/FP32/se-inception.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2143580" vmpeak="2228777" vmrss="776235" vmhwm="1301762" />
<model path="public/se-inception/FP32/se-inception.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1300665" vmpeak="1301435" vmrss="245502" vmhwm="245580" />
<model path="public/se-inception/FP32/se-inception.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2243238" vmpeak="2243238" vmrss="790270" vmhwm="1304394" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="2096530" vmpeak="2096530" vmrss="1029132" vmhwm="1029132" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2468076" vmpeak="2622978" vmrss="1300416" vmhwm="1992411" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="2459880" vmpeak="2459880" vmrss="1043333" vmhwm="1043333" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2570256" vmpeak="2655452" vmrss="1296074" vmhwm="1986774" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="2810792" vmpeak="2810792" vmrss="1314050" vmhwm="1314050" />
<model path="public/se-resnet-152/FP16/se-resnet-152.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2664776" vmpeak="2749973" vmrss="1311289" vmhwm="1996285" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1758135" vmpeak="1758135" vmrss="751441" vmhwm="751441" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2957671" vmpeak="3295692" vmrss="1624287" vmhwm="2522130" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="1940307" vmpeak="2025103" vmrss="764056" vmhwm="764056" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="3047642" vmpeak="3300466" vmrss="1632966" vmhwm="2531656" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="2641943" vmpeak="2725975" vmrss="1146178" vmhwm="1146178" />
<model path="public/se-resnet-152/FP32/se-resnet-152.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="3143878" vmpeak="3567184" vmrss="1643252" vmhwm="2516659" />
<model path="public/se-resnet-50/FP16/se-resnet-50.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1383584" vmpeak="1383584" vmrss="457600" vmhwm="457600" />
<model path="public/se-resnet-50/FP16/se-resnet-50.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2084934" vmpeak="2169668" vmrss="758685" vmhwm="1308013" />
<model path="public/se-resnet-50/FP16/se-resnet-50.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1384640" vmpeak="1384640" vmrss="470329" vmhwm="470329" />
@ -558,18 +522,6 @@
<model path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2053833" vmpeak="2139030" vmrss="524711" vmhwm="830689" />
<model path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1203155" vmpeak="1204528" vmrss="172775" vmhwm="172775" />
<model path="public/ssd_mobilenet_v1_coco/FP32/ssd_mobilenet_v1_coco.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2162082" vmpeak="2162082" vmrss="531549" vmhwm="830174" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1235457" vmpeak="1235457" vmrss="315614" vmhwm="315614" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1984278" vmpeak="2039928" vmrss="651861" vmhwm="1126507" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="1236830" vmpeak="1236830" vmrss="330236" vmhwm="330236" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2071986" vmpeak="2157183" vmrss="668376" vmhwm="1117922" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="1434695" vmpeak="1436068" vmrss="377026" vmhwm="377026" />
<model path="public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2168114" vmpeak="2168114" vmrss="674668" vmhwm="1124229" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1137463" vmpeak="1137463" vmrss="216476" vmhwm="216476" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2099006" vmpeak="2179741" vmrss="753771" vmhwm="1237007" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="1138836" vmpeak="1138836" vmrss="231826" vmhwm="231826" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2184249" vmpeak="2269446" vmrss="734739" vmhwm="1210882" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1393657" vmpeak="1395030" vmrss="336133" vmhwm="336133" />
<model path="public/ssd_mobilenet_v2_coco/FP32/ssd_mobilenet_v2_coco.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2291837" vmpeak="2377034" vmrss="792272" vmhwm="1243455" />
<model path="public/vgg19/FP16/vgg19.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="3373182" vmpeak="3373182" vmrss="2465569" vmhwm="2465569" />
<model path="public/vgg19/FP16/vgg19.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2583620" vmpeak="3276847" vmrss="1036204" vmhwm="1596748" />
<model path="public/vgg19/FP16/vgg19.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="3736839" vmpeak="3736839" vmrss="2498802" vmhwm="2498802" />

12
tests/stress_tests/.automation/memcheck_tests/nightly_configs/desktop_test_config.xml
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@ -12,17 +12,14 @@
<model name="se-inception" precision="FP32" source="omz" />
<model name="efficientnet-b0" precision="FP32" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP32" source="omz" />
<model name="se-resnet-152" precision="FP32" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP32" source="omz" />
<model name="googlenet-v3" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP32" source="omz" />
<model name="alexnet" precision="FP32" source="omz" />
<model name="googlenet-v4-tf" precision="FP32" source="omz" />
<model name="ssd300" precision="FP32" source="omz" />
<model name="vgg19" precision="FP32" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP32" source="omz" />
<model name="googlenet-v1" precision="FP32" source="omz" />
<model name="yolo-v3-tf" precision="FP32" source="omz" />
<model name="mtcnn-o" precision="FP32" source="omz" />
@ -30,7 +27,6 @@
<model name="googlenet-v1-tf" precision="FP32" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP32" source="omz" />
<model name="ssd512" precision="FP32" source="omz" />
<model name="densenet-169" precision="FP32" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP32" source="omz" />
<model name="Sphereface" precision="FP32" source="omz" />
<model name="googlenet-v2" precision="FP32" source="omz" />
@ -52,17 +48,14 @@
<model name="se-inception" precision="FP16" source="omz" />
<model name="efficientnet-b0" precision="FP16" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP16" source="omz" />
<model name="se-resnet-152" precision="FP16" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP16" source="omz" />
<model name="googlenet-v3" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP16" source="omz" />
<model name="alexnet" precision="FP16" source="omz" />
<model name="googlenet-v4-tf" precision="FP16" source="omz" />
<model name="ssd300" precision="FP16" source="omz" />
<model name="vgg19" precision="FP16" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP16" source="omz" />
<model name="googlenet-v1" precision="FP16" source="omz" />
<model name="yolo-v3-tf" precision="FP16" source="omz" />
<model name="mtcnn-o" precision="FP16" source="omz" />
@ -70,7 +63,6 @@
<model name="googlenet-v1-tf" precision="FP16" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP16" source="omz" />
<model name="ssd512" precision="FP16" source="omz" />
<model name="densenet-169" precision="FP16" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP16" source="omz" />
<model name="Sphereface" precision="FP16" source="omz" />
<model name="googlenet-v2" precision="FP16" source="omz" />

12
tests/stress_tests/.automation/memcheck_tests/nightly_configs/myriad_test_config.xml
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@ -11,17 +11,14 @@
<model name="se-inception" precision="FP32" source="omz" />
<model name="efficientnet-b0" precision="FP32" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP32" source="omz" />
<model name="se-resnet-152" precision="FP32" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP32" source="omz" />
<model name="googlenet-v3" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP32" source="omz" />
<model name="alexnet" precision="FP32" source="omz" />
<model name="googlenet-v4-tf" precision="FP32" source="omz" />
<model name="ssd300" precision="FP32" source="omz" />
<model name="vgg19" precision="FP32" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP32" source="omz" />
<model name="googlenet-v1" precision="FP32" source="omz" />
<model name="yolo-v3-tf" precision="FP32" source="omz" />
<model name="mtcnn-o" precision="FP32" source="omz" />
@ -29,7 +26,6 @@
<model name="googlenet-v1-tf" precision="FP32" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP32" source="omz" />
<model name="ssd512" precision="FP32" source="omz" />
<model name="densenet-169" precision="FP32" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP32" source="omz" />
<model name="Sphereface" precision="FP32" source="omz" />
<model name="googlenet-v2" precision="FP32" source="omz" />
@ -51,17 +47,14 @@
<model name="se-inception" precision="FP16" source="omz" />
<model name="efficientnet-b0" precision="FP16" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP16" source="omz" />
<model name="se-resnet-152" precision="FP16" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP16" source="omz" />
<model name="googlenet-v3" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP16" source="omz" />
<model name="alexnet" precision="FP16" source="omz" />
<model name="googlenet-v4-tf" precision="FP16" source="omz" />
<model name="ssd300" precision="FP16" source="omz" />
<model name="vgg19" precision="FP16" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP16" source="omz" />
<model name="googlenet-v1" precision="FP16" source="omz" />
<model name="yolo-v3-tf" precision="FP16" source="omz" />
<model name="mtcnn-o" precision="FP16" source="omz" />
@ -69,7 +62,6 @@
<model name="googlenet-v1-tf" precision="FP16" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP16" source="omz" />
<model name="ssd512" precision="FP16" source="omz" />
<model name="densenet-169" precision="FP16" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP16" source="omz" />
<model name="Sphereface" precision="FP16" source="omz" />
<model name="googlenet-v2" precision="FP16" source="omz" />

62
tests/stress_tests/.automation/memcheck_tests/weekly_configs/desktop_test_config.xml
View File

@ -7,7 +7,6 @@
<!--Models with FP32 precision-->
<model name="mobilenet-v2-1.4-224" precision="FP32" source="omz" />
<model name="brain-tumor-segmentation-0001" precision="FP32" source="omz" />
<model name="octave-resnet-101-0.125" precision="FP32" source="omz" />
<model name="faster_rcnn_inception_resnet_v2_atrous_coco" precision="FP32" source="omz" />
<model name="efficientnet-b7_auto_aug" precision="FP32" source="omz" />
<model name="yolo-v2-tf" precision="FP32" source="omz" />
@ -15,35 +14,24 @@
<model name="se-inception" precision="FP32" source="omz" />
<model name="efficientnet-b0" precision="FP32" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP32" source="omz" />
<model name="se-resnet-152" precision="FP32" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP32" source="omz" />
<model name="googlenet-v3" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP32" source="omz" />
<model name="alexnet" precision="FP32" source="omz" />
<model name="license-plate-recognition-barrier-0007" precision="FP32" source="omz" />
<model name="mobilenet-v1-0.50-224" precision="FP32" source="omz" />
<model name="ssd_mobilenet_v1_fpn_coco" precision="FP32" source="omz" />
<model name="vgg16" precision="FP32" source="omz" />
<model name="face-recognition-resnet34-arcface" precision="FP32" source="omz" />
<model name="gmcnn-places2-tf" precision="FP32" source="omz" />
<model name="mobilenet-v1-1.0-224" precision="FP32" source="omz" />
<model name="se-resnet-101" precision="FP32" source="omz" />
<model name="face-detection-retail-0044" precision="FP32" source="omz" />
<model name="face-recognition-mobilefacenet-arcface" precision="FP32" source="omz" />
<model name="vehicle-license-plate-detection-barrier-0123" precision="FP32" source="omz" />
<model name="densenet-161" precision="FP32" source="omz" />
<model name="mask_rcnn_inception_resnet_v2_atrous_coco" precision="FP32" source="omz" />
<model name="octave-resnext-101-0.25" precision="FP32" source="omz" />
<model name="face-recognition-resnet50-arcface" precision="FP32" source="omz" />
<model name="densenet-161-tf" precision="FP32" source="omz" />
<model name="octave-resnet-200-0.125" precision="FP32" source="omz" />
<model name="mtcnn-p" precision="FP32" source="omz" />
<model name="se-resnext-101" precision="FP32" source="omz" />
<model name="efficientnet-b5" precision="FP32" source="omz" />
<model name="densenet-169-tf" precision="FP32" source="omz" />
<model name="densenet-201" precision="FP32" source="omz" />
<model name="se-resnext-50" precision="FP32" source="omz" />
<model name="resnet-50-tf" precision="FP32" source="omz" />
<model name="squeezenet1.1" precision="FP32" source="omz" />
<model name="squeezenet1.0" precision="FP32" source="omz" />
@ -52,21 +40,15 @@
<model name="ssd300" precision="FP32" source="omz" />
<model name="rfcn-resnet101-coco-tf" precision="FP32" source="omz" />
<model name="vgg19" precision="FP32" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP32" source="omz" />
<model name="efficientnet-b0_auto_aug" precision="FP32" source="omz" />
<model name="googlenet-v1" precision="FP32" source="omz" />
<model name="faster_rcnn_inception_v2_coco" precision="FP32" source="omz" />
<model name="mask_rcnn_inception_v2_coco" precision="FP32" source="omz" />
<model name="inception-resnet-v2-tf" precision="FP32" source="omz" />
<model name="deeplabv3" precision="FP32" source="omz" />
<model name="yolo-v3-tf" precision="FP32" source="omz" />
<model name="mtcnn-o" precision="FP32" source="omz" />
<model name="octave-se-resnet-50-0.125" precision="FP32" source="omz" />
<model name="yolo-v1-tiny-tf" precision="FP32" source="omz" />
<model name="googlenet-v1-tf" precision="FP32" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP32" source="omz" />
<model name="ssd512" precision="FP32" source="omz" />
<model name="densenet-169" precision="FP32" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP32" source="omz" />
<model name="Sphereface" precision="FP32" source="omz" />
<model name="googlenet-v2" precision="FP32" source="omz" />
@ -85,27 +67,18 @@
<model name="faster_rcnn_resnet50_coco" precision="FP32" source="omz" />
<model name="se-resnet-50" precision="FP32" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP32" source="omz" />
<model name="octave-resnet-50-0.125" precision="FP32" source="omz" />
<model name="densenet-121-tf" precision="FP32" source="omz" />
<model name="mobilenet-v1-0.50-160" precision="FP32" source="omz" />
<model name="mobilenet-v1-1.0-224" precision="FP32" source="omz" />
<model name="densenet-121" precision="FP32" source="omz" />
<model name="faster_rcnn_resnet101_coco" precision="FP32" source="omz" />
<model name="octave-densenet-121-0.125" precision="FP32" source="omz" />
<model name="colorization-v2" precision="FP32" source="omz" />
<model name="densenet-121-caffe2" precision="FP32" source="omz" />
<model name="efficientnet-b0-pytorch" precision="FP32" source="omz" />
<model name="efficientnet-b5-pytorch" precision="FP32" source="omz" />
<model name="efficientnet-b7-pytorch" precision="FP32" source="omz" />
<model name="googlenet-v3-pytorch" precision="FP32" source="omz" />
<model name="human-pose-estimation-3d-0001" precision="FP32" source="omz" />
<model name="midasnet" precision="FP32" source="omz" />
<model name="mobilenet-v2-pytorch" precision="FP32" source="omz" />
<model name="resnet-18-pytorch" precision="FP32" source="omz" />
<model name="resnet-50-caffe2" precision="FP32" source="omz" />
<model name="resnet-50-pytorch" precision="FP32" source="omz" />
<model name="single-human-pose-estimation-0001" precision="FP32" source="omz" />
<model name="squeezenet1.1-caffe2" precision="FP32" source="omz" />
<model name="vgg19-caffe2" precision="FP32" source="omz" />
<model name="facial-landmarks-35-adas-0002" precision="FP32" source="omz" />
<model name="vehicle-attributes-recognition-barrier-0039" precision="FP32" source="omz" />
<model name="person-detection-action-recognition-0006" precision="FP32" source="omz" />
@ -167,7 +140,6 @@
<!--Models with FP16 precision-->
<model name="mobilenet-v2-1.4-224" precision="FP16" source="omz" />
<model name="brain-tumor-segmentation-0001" precision="FP16" source="omz" />
<model name="octave-resnet-101-0.125" precision="FP16" source="omz" />
<model name="faster_rcnn_inception_resnet_v2_atrous_coco" precision="FP16" source="omz" />
<model name="efficientnet-b7_auto_aug" precision="FP16" source="omz" />
<model name="yolo-v2-tf" precision="FP16" source="omz" />
@ -175,35 +147,24 @@
<model name="se-inception" precision="FP16" source="omz" />
<model name="efficientnet-b0" precision="FP16" source="omz" />
<model name="mobilenet-v1-1.0-224-tf" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet101_atrous_coco" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v1_coco" precision="FP16" source="omz" />
<model name="se-resnet-152" precision="FP16" source="omz" />
<model name="octave-resnext-50-0.25" precision="FP16" source="omz" />
<model name="googlenet-v3" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v2_coco" precision="FP16" source="omz" />
<model name="alexnet" precision="FP16" source="omz" />
<model name="license-plate-recognition-barrier-0007" precision="FP16" source="omz" />
<model name="mobilenet-v1-0.50-224" precision="FP16" source="omz" />
<model name="ssd_mobilenet_v1_fpn_coco" precision="FP16" source="omz" />
<model name="vgg16" precision="FP16" source="omz" />
<model name="face-recognition-resnet34-arcface" precision="FP16" source="omz" />
<model name="gmcnn-places2-tf" precision="FP16" source="omz" />
<model name="mobilenet-v1-1.0-224" precision="FP16" source="omz" />
<model name="se-resnet-101" precision="FP16" source="omz" />
<model name="face-detection-retail-0044" precision="FP16" source="omz" />
<model name="face-recognition-mobilefacenet-arcface" precision="FP16" source="omz" />
<model name="vehicle-license-plate-detection-barrier-0123" precision="FP16" source="omz" />
<model name="densenet-161" precision="FP16" source="omz" />
<model name="mask_rcnn_inception_resnet_v2_atrous_coco" precision="FP16" source="omz" />
<model name="octave-resnext-101-0.25" precision="FP16" source="omz" />
<model name="face-recognition-resnet50-arcface" precision="FP16" source="omz" />
<model name="densenet-161-tf" precision="FP16" source="omz" />
<model name="octave-resnet-200-0.125" precision="FP16" source="omz" />
<model name="mtcnn-p" precision="FP16" source="omz" />
<model name="se-resnext-101" precision="FP16" source="omz" />
<model name="efficientnet-b5" precision="FP16" source="omz" />
<model name="densenet-169-tf" precision="FP16" source="omz" />
<model name="densenet-201" precision="FP16" source="omz" />
<model name="se-resnext-50" precision="FP16" source="omz" />
<model name="resnet-50-tf" precision="FP16" source="omz" />
<model name="squeezenet1.1" precision="FP16" source="omz" />
<model name="squeezenet1.0" precision="FP16" source="omz" />
@ -212,21 +173,15 @@
<model name="ssd300" precision="FP16" source="omz" />
<model name="rfcn-resnet101-coco-tf" precision="FP16" source="omz" />
<model name="vgg19" precision="FP16" source="omz" />
<model name="ctdet_coco_dlav0_384" precision="FP16" source="omz" />
<model name="efficientnet-b0_auto_aug" precision="FP16" source="omz" />
<model name="googlenet-v1" precision="FP16" source="omz" />
<model name="faster_rcnn_inception_v2_coco" precision="FP16" source="omz" />
<model name="mask_rcnn_inception_v2_coco" precision="FP16" source="omz" />
<model name="inception-resnet-v2-tf" precision="FP16" source="omz" />
<model name="deeplabv3" precision="FP16" source="omz" />
<model name="yolo-v3-tf" precision="FP16" source="omz" />
<model name="mtcnn-o" precision="FP16" source="omz" />
<model name="octave-se-resnet-50-0.125" precision="FP16" source="omz" />
<model name="yolo-v1-tiny-tf" precision="FP16" source="omz" />
<model name="googlenet-v1-tf" precision="FP16" source="omz" />
<model name="yolo-v2-tiny-tf" precision="FP16" source="omz" />
<model name="ssd512" precision="FP16" source="omz" />
<model name="densenet-169" precision="FP16" source="omz" />
<model name="brain-tumor-segmentation-0002" precision="FP16" source="omz" />
<model name="Sphereface" precision="FP16" source="omz" />
<model name="googlenet-v2" precision="FP16" source="omz" />
@ -245,28 +200,19 @@
<model name="faster_rcnn_resnet50_coco" precision="FP16" source="omz" />
<model name="se-resnet-50" precision="FP16" source="omz" />
<model name="mask_rcnn_resnet50_atrous_coco" precision="FP16" source="omz" />
<model name="octave-resnet-50-0.125" precision="FP16" source="omz" />
<model name="densenet-121-tf" precision="FP16" source="omz" />
<model name="mobilenet-v1-0.50-160" precision="FP16" source="omz" />
<model name="mobilenet-v1-1.0-224" precision="FP16" source="omz" />
<model name="densenet-121" precision="FP16" source="omz" />
<model name="faster_rcnn_resnet101_coco" precision="FP16" source="omz" />
<model name="octave-densenet-121-0.125" precision="FP16" source="omz" />
<model name="colorization-v2" precision="FP16" source="omz" />
<model name="densenet-121-caffe2" precision="FP16" source="omz" />
<model name="efficientnet-b0-pytorch" precision="FP16" source="omz" />
<model name="efficientnet-b5-pytorch" precision="FP16" source="omz" />
<model name="efficientnet-b7-pytorch" precision="FP16" source="omz" />
<model name="googlenet-v3-pytorch" precision="FP16" source="omz" />
<model name="human-pose-estimation-3d-0001" precision="FP16" source="omz" />
<model name="asl-recognition-0004" precision="FP16" source="omz" />
<model name="midasnet" precision="FP16" source="omz" />
<model name="mobilenet-v2-pytorch" precision="FP16" source="omz" />
<model name="resnet-18-pytorch" precision="FP16" source="omz" />
<model name="resnet-50-caffe2" precision="FP16" source="omz" />
<model name="resnet-50-pytorch" precision="FP16" source="omz" />
<model name="single-human-pose-estimation-0001" precision="FP16" source="omz" />
<model name="squeezenet1.1-caffe2" precision="FP16" source="omz" />
<model name="vgg19-caffe2" precision="FP16" source="omz" />
<!--Models with FP16-INT8 precision-->
<model name="facial-landmarks-35-adas-0002" precision="FP16-INT8" source="omz" />
<model name="vehicle-attributes-recognition-barrier-0039" precision="FP16-INT8" source="omz" />