From: Maksim Shabunin Date: Wed, 21 Dec 2022 12:37:14 +0000 (+0300) Subject: dnn: updated YOLOv4-tiny model and tests X-Git-Tag: accepted/tizen/unified/20230127.161057~1^2~19^2 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=d35fbe6bfc6344682e8bca882ad1349278ae7c78;p=platform%2Fupstream%2Fopencv.git dnn: updated YOLOv4-tiny model and tests --- diff --git a/modules/dnn/perf/perf_net.cpp b/modules/dnn/perf/perf_net.cpp index 2fb150bb35..cfbb45b173 100644 --- a/modules/dnn/perf/perf_net.cpp +++ b/modules/dnn/perf/perf_net.cpp @@ -251,7 +251,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny) cvtColor(sample, sample, COLOR_BGR2RGB); Mat inp; sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0); - processNet("dnn/yolov4-tiny.weights", "dnn/yolov4-tiny.cfg", "", inp); + processNet("dnn/yolov4-tiny-2020-12.weights", "dnn/yolov4-tiny-2020-12.cfg", "", inp); } PERF_TEST_P_(DNNTestNetwork, EAST_text_detection) diff --git a/modules/dnn/test/test_darknet_importer.cpp b/modules/dnn/test/test_darknet_importer.cpp index 4d11193d96..0b4c1bccff 100644 --- a/modules/dnn/test/test_darknet_importer.cpp +++ b/modules/dnn/test/test_darknet_importer.cpp @@ -562,12 +562,12 @@ TEST_P(Test_Darknet_nets_async, Accuracy) l1 = 0.001; lInf = 0.005; } - if (INF_ENGINE_VER_MAJOR_EQ(2021040000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny") // FIXIT: 4.x only, 3.4 branch works well + if (INF_ENGINE_VER_MAJOR_EQ(2021040000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny-2020-12") // FIXIT: 4.x only, 3.4 branch works well { l1 = 0.001; lInf = 0.005; } - if (INF_ENGINE_VER_MAJOR_EQ(2022010000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny") // FIXIT: 4.x only, 3.4 branch works well + if (INF_ENGINE_VER_MAJOR_EQ(2022010000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny-2020-12") // FIXIT: 4.x only, 3.4 branch works well { l1 = 0.001; lInf = 0.005; @@ -594,7 +594,7 @@ TEST_P(Test_Darknet_nets_async, Accuracy) } INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine( - Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4", "yolov4-tiny"), + Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4", "yolov4-tiny-2020-12"), dnnBackendsAndTargets() )); @@ -827,25 +827,26 @@ TEST_P(Test_Darknet_nets, YOLOv4_tiny) const double confThreshold = 0.5; // batchId, classId, confidence, left, top, right, bottom - const int N0 = 2; + const int N0 = 3; const int N1 = 3; static const float ref_[/* (N0 + N1) * 7 */] = { -0, 7, 0.85935f, 0.593484f, 0.141211f, 0.920356f, 0.291593f, -0, 16, 0.795188f, 0.169207f, 0.386886f, 0.423753f, 0.933004f, +0, 16, 0.889883f, 0.177204f, 0.356279f, 0.417204f, 0.937517f, +0, 7, 0.816615f, 0.604293f, 0.137345f, 0.918016f, 0.295708f, +0, 1, 0.595912f, 0.0940107f, 0.178122f, 0.750619f, 0.829336f, -1, 2, 0.996832f, 0.653802f, 0.464573f, 0.815193f, 0.653292f, -1, 2, 0.963325f, 0.451151f, 0.458915f, 0.496255f, 0.52241f, -1, 0, 0.926244f, 0.194851f, 0.361743f, 0.260277f, 0.632364f, +1, 2, 0.998224f, 0.652883f, 0.463477f, 0.813952f, 0.657163f, +1, 2, 0.967396f, 0.4539f, 0.466368f, 0.497716f, 0.520299f, +1, 0, 0.807866f, 0.205039f, 0.361842f, 0.260984f, 0.643621f, }; Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_); - double scoreDiff = 0.01f; + double scoreDiff = 0.012f; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.15 : 0.01f; if (target == DNN_TARGET_CUDA_FP16) iouDiff = 0.02; - std::string config_file = "yolov4-tiny.cfg"; - std::string weights_file = "yolov4-tiny.weights"; + std::string config_file = "yolov4-tiny-2020-12.cfg"; + std::string weights_file = "yolov4-tiny-2020-12.weights"; #if defined(INF_ENGINE_RELEASE) if (target == DNN_TARGET_MYRIAD) // bad accuracy diff --git a/modules/dnn/test/test_int8_layers.cpp b/modules/dnn/test/test_int8_layers.cpp index 1cafa22619..3551ee239f 100644 --- a/modules/dnn/test/test_int8_layers.cpp +++ b/modules/dnn/test/test_int8_layers.cpp @@ -1320,19 +1320,19 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny) const int N0 = 2; const int N1 = 3; static const float ref_[/* (N0 + N1) * 7 */] = { -0, 7, 0.85935f, 0.593484f, 0.141211f, 0.920356f, 0.291593f, -0, 16, 0.795188f, 0.169207f, 0.386886f, 0.423753f, 0.933004f, +0, 16, 0.912199f, 0.169926f, 0.350896f, 0.422704f, 0.941837f, +0, 7, 0.845388f, 0.617568f, 0.13961f, 0.9008f, 0.29315f, -1, 2, 0.996832f, 0.653802f, 0.464573f, 0.815193f, 0.653292f, -1, 2, 0.963325f, 0.451151f, 0.458915f, 0.496255f, 0.52241f, -1, 0, 0.926244f, 0.194851f, 0.361743f, 0.260277f, 0.632364f, +1, 2, 0.997789f, 0.657455f, 0.459714f, 0.809122f, 0.656829f, +1, 2, 0.924423f, 0.442872f, 0.470127f, 0.49816f, 0.516516f, +1, 0, 0.728307f, 0.202607f, 0.369828f, 0.259445f, 0.613846f, }; Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_); - std::string config_file = "yolov4-tiny.cfg"; - std::string weights_file = "yolov4-tiny.weights"; + std::string config_file = "yolov4-tiny-2020-12.cfg"; + std::string weights_file = "yolov4-tiny-2020-12.weights"; double scoreDiff = 0.12; - double iouDiff = target == DNN_TARGET_OPENCL_FP16 ? 0.2 : 0.082; + double iouDiff = target == DNN_TARGET_OPENCL_FP16 ? 0.2 : 0.118; { SCOPED_TRACE("batch size 1"); @@ -1340,7 +1340,7 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny) { SCOPED_TRACE("Per-tensor quantize"); - testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, 0.16, 0.7, 0.4, false); + testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, 0.224, 0.7, 0.4, false); } }