├── .gitattributes ├── .gitignore ├── README.md └── optFlowFarneback ├── .vs └── optFlowFarneback │ └── v14 │ └── .suo ├── optFlowFarneback.sdf ├── optFlowFarneback.sln ├── optFlowFarneback.v12.suo ├── optFlowFarneback ├── DNN.h ├── Source.cpp ├── bvlc_googlenet.caffemodel ├── bvlc_googlenet.prototxt ├── dcks.mp4 ├── optFlowFarneback.vcxproj ├── optFlowFarneback.vcxproj.filters ├── readME.txt ├── surv4.mp4 ├── synset_words.txt ├── traffic.mp4 ├── train_val.prototxt └── x64 │ ├── Debug │ ├── Source.obj │ ├── optFlowFarneback.log │ ├── optFlowFarneback.tlog │ │ ├── CL.read.1.tlog │ │ ├── CL.write.1.tlog │ │ ├── cl.command.1.tlog │ │ ├── link.command.1.tlog │ │ ├── link.read.1.tlog │ │ ├── link.write.1.tlog │ │ └── optFlowFarneback.lastbuildstate │ ├── vc120.idb │ └── vc120.pdb │ └── Release │ ├── Source.obj │ ├── optFlowFarneback.log │ ├── optFlowFarneback.tlog │ ├── CL.command.1.tlog │ ├── cl.read.1.tlog │ ├── cl.write.1.tlog │ ├── link.command.1.tlog │ ├── link.read.1.tlog │ ├── link.write.1.tlog │ └── optFlowFarneback.lastbuildstate │ ├── vc120.pdb │ └── vc140.pdb └── x64 ├── Debug ├── optFlowFarneback.exe ├── optFlowFarneback.ilk └── optFlowFarneback.pdb └── Release ├── optFlowFarneback.exe ├── optFlowFarneback.iobj ├── optFlowFarneback.ipdb └── optFlowFarneback.pdb /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear in the root of a volume 35 | .DocumentRevisions-V100 36 | .fseventsd 37 | .Spotlight-V100 38 | .TemporaryItems 39 | .Trashes 40 | .VolumeIcon.icns 41 | 42 | # Directories potentially created on remote AFP share 43 | .AppleDB 44 | .AppleDesktop 45 | Network Trash Folder 46 | Temporary Items 47 | .apdisk 48 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition 2 | Farneback flow algorithm to segment motion and classify the ROI using CNN - Caffe model 3 | 4 | #Visit the github page for this project - for implementation details and the demo video 5 | https://bassamarshad.github.io/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/ 6 | 7 | -------------------------------------------------------------------------------- /optFlowFarneback/.vs/optFlowFarneback/v14/.suo: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/.vs/optFlowFarneback/v14/.suo -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback.sdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback.sdf -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback.sln: -------------------------------------------------------------------------------- 1 |  2 | Microsoft Visual Studio Solution File, Format Version 12.00 3 | # Visual Studio 14 4 | VisualStudioVersion = 14.0.23107.0 5 | MinimumVisualStudioVersion = 10.0.40219.1 6 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "optFlowFarneback", "optFlowFarneback\optFlowFarneback.vcxproj", "{F58B092C-1690-4609-AF57-401FEE939829}" 7 | EndProject 8 | Global 9 | GlobalSection(SolutionConfigurationPlatforms) = preSolution 10 | Debug|x64 = Debug|x64 11 | Debug|x86 = Debug|x86 12 | Release|x64 = Release|x64 13 | Release|x86 = Release|x86 14 | EndGlobalSection 15 | GlobalSection(ProjectConfigurationPlatforms) = postSolution 16 | {F58B092C-1690-4609-AF57-401FEE939829}.Debug|x64.ActiveCfg = Debug|x64 17 | {F58B092C-1690-4609-AF57-401FEE939829}.Debug|x64.Build.0 = Debug|x64 18 | {F58B092C-1690-4609-AF57-401FEE939829}.Debug|x86.ActiveCfg = Debug|Win32 19 | {F58B092C-1690-4609-AF57-401FEE939829}.Debug|x86.Build.0 = Debug|Win32 20 | {F58B092C-1690-4609-AF57-401FEE939829}.Release|x64.ActiveCfg = Release|x64 21 | {F58B092C-1690-4609-AF57-401FEE939829}.Release|x64.Build.0 = Release|x64 22 | {F58B092C-1690-4609-AF57-401FEE939829}.Release|x86.ActiveCfg = Release|Win32 23 | {F58B092C-1690-4609-AF57-401FEE939829}.Release|x86.Build.0 = Release|Win32 24 | EndGlobalSection 25 | GlobalSection(SolutionProperties) = preSolution 26 | HideSolutionNode = FALSE 27 | EndGlobalSection 28 | EndGlobal 29 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback.v12.suo: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback.v12.suo -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/DNN.h: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | using namespace cv; 5 | using namespace cv::dnn; 6 | 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | using namespace std; 13 | 14 | struct probLabel 15 | { 16 | string label; 17 | float probability; 18 | }; 19 | 20 | 21 | class DNN 22 | { 23 | 24 | public: 25 | 26 | 27 | 28 | /* Find best class for the blob (i. e. class with maximal probability) */ 29 | void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb) 30 | { 31 | Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix 32 | Point classNumber; 33 | 34 | minMaxLoc(probMat, NULL, classProb, NULL, &classNumber); 35 | *classId = classNumber.x; 36 | } 37 | 38 | std::vector readClassNames(const char *filename = "synset_words.txt") 39 | { 40 | std::vector classNames; 41 | 42 | std::ifstream fp(filename); 43 | if (!fp.is_open()) 44 | { 45 | std::cerr << "File with classes labels not found: " << filename << std::endl; 46 | exit(-1); 47 | } 48 | 49 | std::string name; 50 | while (!fp.eof()) 51 | { 52 | std::getline(fp, name); 53 | if (name.length()) 54 | classNames.push_back(name.substr(name.find(' ') + 1)); 55 | } 56 | 57 | fp.close(); 58 | return classNames; 59 | } 60 | 61 | probLabel predictLabel(Mat img) 62 | { 63 | String modelTxt = "bvlc_googlenet.prototxt"; 64 | String modelBin = "bvlc_googlenet.caffemodel"; 65 | //String imageFile = (argc > 1) ? argv[1] : "3.jpg"; 66 | 67 | //! [Create the importer of Caffe model] 68 | Ptr importer; 69 | try //Try to import Caffe GoogleNet model 70 | { 71 | importer = dnn::createCaffeImporter(modelTxt, modelBin); 72 | } 73 | catch (const cv::Exception &err) //Importer can throw errors, we will catch them 74 | { 75 | std::cerr << err.msg << std::endl; 76 | } 77 | //! [Create the importer of Caffe model] 78 | 79 | if (!importer) 80 | { 81 | std::cerr << "Can't load network by using the following files: " << std::endl; 82 | std::cerr << "prototxt: " << modelTxt << std::endl; 83 | std::cerr << "caffemodel: " << modelBin << std::endl; 84 | std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl; 85 | std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl; 86 | exit(-1); 87 | } 88 | 89 | //! [Initialize network] 90 | dnn::Net net; 91 | importer->populateNet(net); 92 | importer.release(); //We don't need importer anymore 93 | //! [Initialize network] 94 | 95 | //! [Prepare blob] 96 | //Mat img = imread(imageFile); 97 | //if (img.empty()) 98 | //{ 99 | // std::cerr << "Can't read image from the file: " << imageFile << std::endl; 100 | // exit(-1); 101 | //} 102 | 103 | resize(img, img, Size(224, 224)); //GoogLeNet accepts only 224x224 RGB-images 104 | dnn::Blob inputBlob = dnn::Blob(img); //Convert Mat to dnn::Blob image batch 105 | //! [Prepare blob] 106 | 107 | //! [Set input blob] 108 | net.setBlob(".data", inputBlob); //set the network input 109 | //! [Set input blob] 110 | 111 | //! [Make forward pass] 112 | net.forward(); //compute output 113 | //! [Make forward pass] 114 | 115 | //! [Gather output] 116 | dnn::Blob prob = net.getBlob("prob"); //gather output of "prob" layer 117 | 118 | int classId; 119 | double classProb; 120 | getMaxClass(prob, &classId, &classProb);//find the best class 121 | //! [Gather output] 122 | 123 | probLabel obj; 124 | 125 | 126 | //! [Print results] 127 | std::vector classNames = readClassNames(); 128 | std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl; 129 | std::cout << "Probability: " << classProb * 100 << "%" << std::endl; 130 | //! [Print results] 131 | 132 | obj.label = classNames.at(classId); 133 | obj.probability = classProb * 100; 134 | 135 | return obj; 136 | } //main 137 | 138 | }; -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/Source.cpp: -------------------------------------------------------------------------------- 1 | #include "opencv2/video/tracking.hpp" 2 | #include "opencv2/imgproc/imgproc.hpp" 3 | #include "opencv2/videoio/videoio.hpp" 4 | #include "opencv2/highgui/highgui.hpp" 5 | #include 6 | #include "opencv2/features2d/features2d.hpp" 7 | #include "opencv2/optflow/motempl.hpp" 8 | //#include "Python.h" 9 | #include 10 | #include "opencv2/video.hpp" 11 | /* 12 | #include "opencv2/cudaoptflow.hpp" 13 | #include "opencv2/cudaarithm.hpp" 14 | #include 15 | #include 16 | */ 17 | #include 18 | #include 19 | #include "DNN.h" 20 | 21 | using namespace cv; 22 | using namespace std; 23 | using namespace cv::dnn; 24 | //using namespace cv::cuda; 25 | 26 | 27 | Mat findBlobs(Mat src,Mat original,int frameCount) ; 28 | Mat findBlobsWithLabels(Mat src, Mat original, int frameCount); 29 | 30 | DNN dnn1; 31 | probLabel pl; 32 | 33 | static void help() 34 | { 35 | cout << 36 | "\nThis program demonstrates dense optical flow algorithm by Gunnar Farneback\n" 37 | "Mainly the function: calcOpticalFlowFarneback()\n" 38 | "Call:\n" 39 | "./fback\n" 40 | "This reads from video camera 0\n" << endl; 41 | } 42 | static void drawOptFlowMap(const Mat& flow, Mat& cflowmap, int step, 43 | double, const Scalar& color) 44 | { 45 | for (int y = 0; y < cflowmap.rows; y += step) 46 | for (int x = 0; x < cflowmap.cols; x += step) 47 | { 48 | const Point2f& fxy = flow.at(y, x); 49 | line(cflowmap, Point(x, y), Point(cvRound(x + fxy.x), cvRound(y + fxy.y)), 50 | color); 51 | circle(cflowmap, Point(x, y), 2, color, -1); 52 | } 53 | } 54 | 55 | int optMag=4; 56 | RNG rng(12345); 57 | 58 | float MHI_DURATION = 0.05; 59 | 60 | 61 | 62 | 63 | 64 | int main(int argc, char** argv) 65 | { 66 | namedWindow("Magnitude Thresh Trackbar", 0); 67 | //setMouseCallback("S-V Trackbar", onMouse, 0); 68 | createTrackbar("Optical Flow Magnitude", "Magnitude Thresh Trackbar", &optMag, 50, 0); 69 | 70 | cv::CommandLineParser parser(argc, argv, "{help h||}"); 71 | if (parser.has("help")) 72 | { 73 | help(); 74 | return 0; 75 | } 76 | 77 | VideoCapture cap; 78 | 79 | Mat frame1, ret, motion_mask; 80 | cap.read(frame1); 81 | 82 | Size frame_size = frame1.size(); 83 | int h = frame_size.height; 84 | int w = frame_size.width; 85 | 86 | //Mat motion_history(h, w, CV_32FC1, Scalar(0, 0, 0)); 87 | Mat seg_mask(h, w, CV_32FC1, Scalar(0, 0, 0)); 88 | vector seg_bounds; 89 | 90 | cap.open(0); 91 | //cap.open("volleyball.mp4"); 92 | cout << "\n Camera Frame width \n" << cap.get(CV_CAP_PROP_FRAME_WIDTH); 93 | cout << "Camera Frame height \n" << cap.get(CV_CAP_PROP_FRAME_HEIGHT); 94 | help(); 95 | if (!cap.isOpened()) 96 | return -1; 97 | 98 | Mat flow, cflow, frame,lastFrame; 99 | UMat gray, prevgray, uflow; 100 | //namedWindow("flow", 1); 101 | 102 | Mat res; 103 | int frameCount = 0; 104 | bool useGPU = false; 105 | 106 | //cv::cuda::GpuMat d_flow; 107 | //cv::Ptr d_calc = cv::cuda::FarnebackOpticalFlow::create(); 108 | 109 | //WE initialze the frame size over here 110 | // 780p resolution : 1280x720 111 | //1080p resoltuion : 1920 x 1080 112 | Size videoSize = Size(1280, 720); 113 | 114 | 115 | VideoWriter motionVideo("MotionDetectedObjects.wmv", CV_FOURCC('W', 'M', 'V', '1'), 10, videoSize, true); 116 | VideoWriter labelVideo("ObjectLabels.wmv", CV_FOURCC('W', 'M', 'V', '1'), 10, videoSize, true); 117 | 118 | 119 | Mat labelRect; 120 | for (;;) 121 | { 122 | 123 | cap >> frame; 124 | if (!cap.read(frame)) 125 | break; 126 | 127 | resize(frame, frame, videoSize, 0, 0, INTER_CUBIC); 128 | cvtColor(frame, gray, COLOR_BGR2GRAY); 129 | 130 | if (!prevgray.empty()) 131 | { 132 | 133 | Mat xy[2]; 134 | 135 | /* 136 | if (useGPU) 137 | { 138 | cv::cuda::GpuMat d_frameL(prevgray), d_frameR(gray); 139 | 140 | d_calc->calc(d_frameL, d_frameR, d_flow); 141 | 142 | cuda::GpuMat planes[2]; 143 | cuda::split(d_flow, planes); 144 | 145 | planes[0].download(xy[0]); 146 | planes[1].download(xy[1]); 147 | } 148 | else 149 | { 150 | */ 151 | calcOpticalFlowFarneback(prevgray, gray, uflow, 0.5, 3, 15, 3, 5, 1.2, 0); 152 | cvtColor(prevgray, cflow, COLOR_GRAY2BGR); 153 | 154 | uflow.copyTo(flow); 155 | 156 | //drawOptFlowMap(flow, cflow, 8, 1.5, Scalar(0, 255, 0)); 157 | //imshow("flow", cflow); 158 | split(flow, xy); 159 | //} 160 | 161 | //calculate angle and magnitude 162 | Mat magnitude, angle; 163 | cv::cartToPolar(xy[0], xy[1], magnitude, angle, true); 164 | 165 | 166 | //translate magnitude to range [0;1] 167 | //double mag_max; 168 | //minMaxLoc(magnitude, 0, &mag_max); 169 | //magnitude.convertTo(magnitude, -1, 1.0 / mag_max); 170 | 171 | 172 | cv::threshold(magnitude, res, optMag,100, CV_THRESH_BINARY); 173 | imshow("flow magnitude", res); 174 | 175 | Mat res2; 176 | res.convertTo(res2, CV_8UC1, 255); 177 | 178 | // Create a structuring element (SE) and do some morphological operation in order to close holes, get unified connected components 179 | int morph_size = 2; 180 | Mat element = getStructuringElement(MORPH_ELLIPSE, Size(2 * morph_size + 1, 2 * morph_size + 1), Point(morph_size, morph_size)); 181 | morphologyEx(res2, res2, MORPH_CLOSE, element, Point(-1, -1), 12); 182 | //morphologyEx(src, src, MORPH_ERODE, element, Point(-1, -1), 8); 183 | 184 | /* 185 | //Motion History and segmentMotion 186 | motion_mask = res2.clone(); 187 | 188 | // Create a structuring element (SE) 189 | int morph_size = 2; 190 | Mat element = getStructuringElement(MORPH_ELLIPSE, Size(2 * morph_size + 1, 2 * morph_size + 1), Point(morph_size, morph_size)); 191 | morphologyEx(motion_mask, motion_mask, MORPH_CLOSE, element, Point(-1, -1), 4); 192 | 193 | double timestamp = 1000.0*clock() / CLOCKS_PER_SEC; 194 | 195 | Mat motion_history(frame.rows, frame.cols, CV_32FC1, Scalar(0, 0, 0)); 196 | cv::motempl::updateMotionHistory(motion_mask, motion_history, timestamp, MHI_DURATION); 197 | cv::motempl::segmentMotion(motion_history, seg_mask, seg_bounds, timestamp, 10000); 198 | 199 | 200 | 201 | for (int i = 0; i < seg_bounds.size(); i++) 202 | { 203 | if(seg_bounds[i].area() > 3000) 204 | { 205 | Mat ROI = frame(seg_bounds[i]); 206 | String fileName = to_string(frameCount) + ".jpg"; 207 | imwrite(fileName, ROI); 208 | rectangle(frame, seg_bounds[i], CV_RGB(255, 0, 0), 2, 8); 209 | } 210 | } 211 | 212 | Mat seg1; 213 | seg_mask.convertTo(seg1, CV_8UC1); 214 | imshow("rects", frame); 215 | imshow("segMask", seg1); 216 | seg_bounds.clear(); 217 | */ 218 | 219 | 220 | 221 | Mat motionRect=findBlobs(res2, frame, frameCount); 222 | motionVideo.write(motionRect); 223 | 224 | 225 | if (frameCount%5==0) 226 | { 227 | labelRect=findBlobsWithLabels(res2, frame, frameCount); 228 | labelVideo.write(labelRect); 229 | } 230 | else 231 | labelVideo.write(labelRect); 232 | 233 | } 234 | if (waitKey(10) >= 0) 235 | break; 236 | 237 | char c = (char)waitKey(1); 238 | if (c == 27) 239 | break; 240 | switch (c) 241 | { 242 | case 'e': 243 | break; 244 | } 245 | 246 | std::swap(prevgray, gray); 247 | //std::swap(lastFrame, frame); 248 | frameCount++; 249 | } 250 | return 0; 251 | } 252 | 253 | Mat findBlobs(Mat src,Mat original,int frameCount) { 254 | 255 | Mat dst; 256 | cvtColor(src, dst, CV_GRAY2BGR); 257 | 258 | //wifi passwd - c10c5b03 259 | 260 | vector> contours; // storing contour 261 | vector hierarchy; 262 | 263 | findContours(src.clone(), contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); 264 | 265 | Mat localFrame = original.clone(); 266 | vector areas(contours.size()); 267 | 268 | 269 | for (int i = 0; i < contours.size(); i++) { 270 | areas[i] = contourArea(Mat(contours[i])); 271 | if (areas[i] > 1500) 272 | { 273 | Rect rect1 = boundingRect(contours[i]); 274 | rectangle(dst, rect1, CV_RGB(255, 0, 0), 2, 8); 275 | rectangle(localFrame, rect1, CV_RGB(255, 0, 0), 2, 8); 276 | 277 | //Below was to store the ROI in the project directory ! 278 | //Mat ROI = original(rect1); 279 | //String fileName = to_string(frameCount) + ".jpg"; 280 | //imwrite(fileName, ROI); 281 | //Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)); 282 | 283 | } 284 | } 285 | 286 | imshow("Blobs Rect", dst); 287 | 288 | imshow("On original", localFrame); 289 | 290 | return localFrame; 291 | 292 | } 293 | 294 | 295 | Mat findBlobsWithLabels(Mat src, Mat original, int frameCount) { 296 | 297 | 298 | vector> contours; // storing contour 299 | vector hierarchy; 300 | 301 | findContours(src.clone(), contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); 302 | 303 | vector areas(contours.size()); 304 | 305 | for (int i = 0; i < contours.size(); i++) { 306 | Rect rect1 = boundingRect(contours[i]); 307 | if (rect1.area()>2000) 308 | { 309 | Mat i1 = original(rect1); 310 | pl = dnn1.predictLabel(i1); 311 | String label = pl.label; 312 | std::stringstream ss; 313 | ss << std::fixed << std::setprecision(2) << pl.probability; 314 | std::string mystring = ss.str(); 315 | String probability = " " + mystring; 316 | if (!mystring.empty() || !label.empty() ) 317 | { 318 | //Below to check if Point above Rect (for label text) is in the Mat/frame 319 | cv::Rect rect(cv::Point(), original.size()); 320 | cv::Point p(rect1.x - 10, rect1.y - 10); 321 | if (rect.contains(p)) 322 | { 323 | putText(original, label + probability, p, FONT_HERSHEY_SIMPLEX, 0.8, CV_RGB(255, 255, 255), 2, CV_AA); 324 | } 325 | else 326 | { 327 | putText(original, label + probability, Point(rect1.x, rect1.y + rect1.height + 20), FONT_HERSHEY_SIMPLEX, 0.8, CV_RGB(255, 255, 255), 2, CV_AA); 328 | } 329 | rectangle(original, rect1, CV_RGB(255, 0, 0), 2, 8); 330 | } 331 | waitKey(10); 332 | } 333 | } 334 | 335 | imshow("Object Labels", original); 336 | 337 | return original; 338 | 339 | 340 | } 341 | 342 | 343 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/bvlc_googlenet.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback/bvlc_googlenet.caffemodel -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/bvlc_googlenet.prototxt: -------------------------------------------------------------------------------- 1 | name: "GoogleNet" 2 | input: "data" 3 | input_dim: 10 4 | input_dim: 3 5 | input_dim: 224 6 | input_dim: 224 7 | layer { 8 | name: "conv1/7x7_s2" 9 | type: "Convolution" 10 | bottom: "data" 11 | top: "conv1/7x7_s2" 12 | param { 13 | lr_mult: 1 14 | decay_mult: 1 15 | } 16 | param { 17 | lr_mult: 2 18 | decay_mult: 0 19 | } 20 | convolution_param { 21 | num_output: 64 22 | pad: 3 23 | kernel_size: 7 24 | stride: 2 25 | weight_filler { 26 | type: "xavier" 27 | std: 0.1 28 | } 29 | bias_filler { 30 | type: "constant" 31 | value: 0.2 32 | } 33 | } 34 | } 35 | layer { 36 | name: "conv1/relu_7x7" 37 | type: "ReLU" 38 | bottom: "conv1/7x7_s2" 39 | top: "conv1/7x7_s2" 40 | } 41 | layer { 42 | name: "pool1/3x3_s2" 43 | type: "Pooling" 44 | bottom: "conv1/7x7_s2" 45 | top: "pool1/3x3_s2" 46 | pooling_param { 47 | pool: MAX 48 | kernel_size: 3 49 | stride: 2 50 | } 51 | } 52 | layer { 53 | name: "pool1/norm1" 54 | type: "LRN" 55 | bottom: "pool1/3x3_s2" 56 | top: "pool1/norm1" 57 | lrn_param { 58 | local_size: 5 59 | alpha: 0.0001 60 | beta: 0.75 61 | } 62 | } 63 | layer { 64 | name: "conv2/3x3_reduce" 65 | type: "Convolution" 66 | bottom: "pool1/norm1" 67 | top: "conv2/3x3_reduce" 68 | param { 69 | lr_mult: 1 70 | decay_mult: 1 71 | } 72 | param { 73 | lr_mult: 2 74 | decay_mult: 0 75 | } 76 | convolution_param { 77 | num_output: 64 78 | kernel_size: 1 79 | weight_filler { 80 | type: "xavier" 81 | std: 0.1 82 | } 83 | bias_filler { 84 | type: "constant" 85 | value: 0.2 86 | } 87 | } 88 | } 89 | layer { 90 | name: "conv2/relu_3x3_reduce" 91 | type: "ReLU" 92 | bottom: "conv2/3x3_reduce" 93 | top: "conv2/3x3_reduce" 94 | } 95 | layer { 96 | name: "conv2/3x3" 97 | type: "Convolution" 98 | bottom: "conv2/3x3_reduce" 99 | top: "conv2/3x3" 100 | param { 101 | lr_mult: 1 102 | decay_mult: 1 103 | } 104 | param { 105 | lr_mult: 2 106 | decay_mult: 0 107 | } 108 | convolution_param { 109 | num_output: 192 110 | pad: 1 111 | kernel_size: 3 112 | weight_filler { 113 | type: "xavier" 114 | std: 0.03 115 | } 116 | bias_filler { 117 | type: "constant" 118 | value: 0.2 119 | } 120 | } 121 | } 122 | layer { 123 | name: "conv2/relu_3x3" 124 | type: "ReLU" 125 | bottom: "conv2/3x3" 126 | top: "conv2/3x3" 127 | } 128 | layer { 129 | name: "conv2/norm2" 130 | type: "LRN" 131 | bottom: "conv2/3x3" 132 | top: "conv2/norm2" 133 | lrn_param { 134 | local_size: 5 135 | alpha: 0.0001 136 | beta: 0.75 137 | } 138 | } 139 | layer { 140 | name: "pool2/3x3_s2" 141 | type: "Pooling" 142 | bottom: "conv2/norm2" 143 | top: "pool2/3x3_s2" 144 | pooling_param { 145 | pool: MAX 146 | kernel_size: 3 147 | stride: 2 148 | } 149 | } 150 | layer { 151 | name: "inception_3a/1x1" 152 | type: "Convolution" 153 | bottom: "pool2/3x3_s2" 154 | top: "inception_3a/1x1" 155 | param { 156 | lr_mult: 1 157 | decay_mult: 1 158 | } 159 | param { 160 | lr_mult: 2 161 | decay_mult: 0 162 | } 163 | convolution_param { 164 | num_output: 64 165 | kernel_size: 1 166 | weight_filler { 167 | type: "xavier" 168 | std: 0.03 169 | } 170 | bias_filler { 171 | type: "constant" 172 | value: 0.2 173 | } 174 | } 175 | } 176 | layer { 177 | name: "inception_3a/relu_1x1" 178 | type: "ReLU" 179 | bottom: "inception_3a/1x1" 180 | top: "inception_3a/1x1" 181 | } 182 | layer { 183 | name: "inception_3a/3x3_reduce" 184 | type: "Convolution" 185 | bottom: "pool2/3x3_s2" 186 | top: "inception_3a/3x3_reduce" 187 | param { 188 | lr_mult: 1 189 | decay_mult: 1 190 | } 191 | param { 192 | lr_mult: 2 193 | decay_mult: 0 194 | } 195 | convolution_param { 196 | num_output: 96 197 | kernel_size: 1 198 | weight_filler { 199 | type: "xavier" 200 | std: 0.09 201 | } 202 | bias_filler { 203 | type: "constant" 204 | value: 0.2 205 | } 206 | } 207 | } 208 | layer { 209 | name: "inception_3a/relu_3x3_reduce" 210 | type: "ReLU" 211 | bottom: "inception_3a/3x3_reduce" 212 | top: "inception_3a/3x3_reduce" 213 | } 214 | layer { 215 | name: "inception_3a/3x3" 216 | type: "Convolution" 217 | bottom: "inception_3a/3x3_reduce" 218 | top: "inception_3a/3x3" 219 | param { 220 | lr_mult: 1 221 | decay_mult: 1 222 | } 223 | param { 224 | lr_mult: 2 225 | decay_mult: 0 226 | } 227 | convolution_param { 228 | num_output: 128 229 | pad: 1 230 | kernel_size: 3 231 | weight_filler { 232 | type: "xavier" 233 | std: 0.03 234 | } 235 | bias_filler { 236 | type: "constant" 237 | value: 0.2 238 | } 239 | } 240 | } 241 | layer { 242 | name: "inception_3a/relu_3x3" 243 | type: "ReLU" 244 | bottom: "inception_3a/3x3" 245 | top: "inception_3a/3x3" 246 | } 247 | layer { 248 | name: "inception_3a/5x5_reduce" 249 | type: "Convolution" 250 | bottom: "pool2/3x3_s2" 251 | top: "inception_3a/5x5_reduce" 252 | param { 253 | lr_mult: 1 254 | decay_mult: 1 255 | } 256 | param { 257 | lr_mult: 2 258 | decay_mult: 0 259 | } 260 | convolution_param { 261 | num_output: 16 262 | kernel_size: 1 263 | weight_filler { 264 | type: "xavier" 265 | std: 0.2 266 | } 267 | bias_filler { 268 | type: "constant" 269 | value: 0.2 270 | } 271 | } 272 | } 273 | layer { 274 | name: "inception_3a/relu_5x5_reduce" 275 | type: "ReLU" 276 | bottom: "inception_3a/5x5_reduce" 277 | top: "inception_3a/5x5_reduce" 278 | } 279 | layer { 280 | name: "inception_3a/5x5" 281 | type: "Convolution" 282 | bottom: "inception_3a/5x5_reduce" 283 | top: "inception_3a/5x5" 284 | param { 285 | lr_mult: 1 286 | decay_mult: 1 287 | } 288 | param { 289 | lr_mult: 2 290 | decay_mult: 0 291 | } 292 | convolution_param { 293 | num_output: 32 294 | pad: 2 295 | kernel_size: 5 296 | weight_filler { 297 | type: "xavier" 298 | std: 0.03 299 | } 300 | bias_filler { 301 | type: "constant" 302 | value: 0.2 303 | } 304 | } 305 | } 306 | layer { 307 | name: "inception_3a/relu_5x5" 308 | type: "ReLU" 309 | bottom: "inception_3a/5x5" 310 | top: "inception_3a/5x5" 311 | } 312 | layer { 313 | name: "inception_3a/pool" 314 | type: "Pooling" 315 | bottom: "pool2/3x3_s2" 316 | top: "inception_3a/pool" 317 | pooling_param { 318 | pool: MAX 319 | kernel_size: 3 320 | stride: 1 321 | pad: 1 322 | } 323 | } 324 | layer { 325 | name: "inception_3a/pool_proj" 326 | type: "Convolution" 327 | bottom: "inception_3a/pool" 328 | top: "inception_3a/pool_proj" 329 | param { 330 | lr_mult: 1 331 | decay_mult: 1 332 | } 333 | param { 334 | lr_mult: 2 335 | decay_mult: 0 336 | } 337 | convolution_param { 338 | num_output: 32 339 | kernel_size: 1 340 | weight_filler { 341 | type: "xavier" 342 | std: 0.1 343 | } 344 | bias_filler { 345 | type: "constant" 346 | value: 0.2 347 | } 348 | } 349 | } 350 | layer { 351 | name: "inception_3a/relu_pool_proj" 352 | type: "ReLU" 353 | bottom: "inception_3a/pool_proj" 354 | top: "inception_3a/pool_proj" 355 | } 356 | layer { 357 | name: "inception_3a/output" 358 | type: "Concat" 359 | bottom: "inception_3a/1x1" 360 | bottom: "inception_3a/3x3" 361 | bottom: "inception_3a/5x5" 362 | bottom: "inception_3a/pool_proj" 363 | top: "inception_3a/output" 364 | } 365 | layer { 366 | name: "inception_3b/1x1" 367 | type: "Convolution" 368 | bottom: "inception_3a/output" 369 | top: "inception_3b/1x1" 370 | param { 371 | lr_mult: 1 372 | decay_mult: 1 373 | } 374 | param { 375 | lr_mult: 2 376 | decay_mult: 0 377 | } 378 | convolution_param { 379 | num_output: 128 380 | kernel_size: 1 381 | weight_filler { 382 | type: "xavier" 383 | std: 0.03 384 | } 385 | bias_filler { 386 | type: "constant" 387 | value: 0.2 388 | } 389 | } 390 | } 391 | layer { 392 | name: "inception_3b/relu_1x1" 393 | type: "ReLU" 394 | bottom: "inception_3b/1x1" 395 | top: "inception_3b/1x1" 396 | } 397 | layer { 398 | name: "inception_3b/3x3_reduce" 399 | type: "Convolution" 400 | bottom: "inception_3a/output" 401 | top: "inception_3b/3x3_reduce" 402 | param { 403 | lr_mult: 1 404 | decay_mult: 1 405 | } 406 | param { 407 | lr_mult: 2 408 | decay_mult: 0 409 | } 410 | convolution_param { 411 | num_output: 128 412 | kernel_size: 1 413 | weight_filler { 414 | type: "xavier" 415 | std: 0.09 416 | } 417 | bias_filler { 418 | type: "constant" 419 | value: 0.2 420 | } 421 | } 422 | } 423 | layer { 424 | name: "inception_3b/relu_3x3_reduce" 425 | type: "ReLU" 426 | bottom: "inception_3b/3x3_reduce" 427 | top: "inception_3b/3x3_reduce" 428 | } 429 | layer { 430 | name: "inception_3b/3x3" 431 | type: "Convolution" 432 | bottom: "inception_3b/3x3_reduce" 433 | top: "inception_3b/3x3" 434 | param { 435 | lr_mult: 1 436 | decay_mult: 1 437 | } 438 | param { 439 | lr_mult: 2 440 | decay_mult: 0 441 | } 442 | convolution_param { 443 | num_output: 192 444 | pad: 1 445 | kernel_size: 3 446 | weight_filler { 447 | type: "xavier" 448 | std: 0.03 449 | } 450 | bias_filler { 451 | type: "constant" 452 | value: 0.2 453 | } 454 | } 455 | } 456 | layer { 457 | name: "inception_3b/relu_3x3" 458 | type: "ReLU" 459 | bottom: "inception_3b/3x3" 460 | top: "inception_3b/3x3" 461 | } 462 | layer { 463 | name: "inception_3b/5x5_reduce" 464 | type: "Convolution" 465 | bottom: "inception_3a/output" 466 | top: "inception_3b/5x5_reduce" 467 | param { 468 | lr_mult: 1 469 | decay_mult: 1 470 | } 471 | param { 472 | lr_mult: 2 473 | decay_mult: 0 474 | } 475 | convolution_param { 476 | num_output: 32 477 | kernel_size: 1 478 | weight_filler { 479 | type: "xavier" 480 | std: 0.2 481 | } 482 | bias_filler { 483 | type: "constant" 484 | value: 0.2 485 | } 486 | } 487 | } 488 | layer { 489 | name: "inception_3b/relu_5x5_reduce" 490 | type: "ReLU" 491 | bottom: "inception_3b/5x5_reduce" 492 | top: "inception_3b/5x5_reduce" 493 | } 494 | layer { 495 | name: "inception_3b/5x5" 496 | type: "Convolution" 497 | bottom: "inception_3b/5x5_reduce" 498 | top: "inception_3b/5x5" 499 | param { 500 | lr_mult: 1 501 | decay_mult: 1 502 | } 503 | param { 504 | lr_mult: 2 505 | decay_mult: 0 506 | } 507 | convolution_param { 508 | num_output: 96 509 | pad: 2 510 | kernel_size: 5 511 | weight_filler { 512 | type: "xavier" 513 | std: 0.03 514 | } 515 | bias_filler { 516 | type: "constant" 517 | value: 0.2 518 | } 519 | } 520 | } 521 | layer { 522 | name: "inception_3b/relu_5x5" 523 | type: "ReLU" 524 | bottom: "inception_3b/5x5" 525 | top: "inception_3b/5x5" 526 | } 527 | layer { 528 | name: "inception_3b/pool" 529 | type: "Pooling" 530 | bottom: "inception_3a/output" 531 | top: "inception_3b/pool" 532 | pooling_param { 533 | pool: MAX 534 | kernel_size: 3 535 | stride: 1 536 | pad: 1 537 | } 538 | } 539 | layer { 540 | name: "inception_3b/pool_proj" 541 | type: "Convolution" 542 | bottom: "inception_3b/pool" 543 | top: "inception_3b/pool_proj" 544 | param { 545 | lr_mult: 1 546 | decay_mult: 1 547 | } 548 | param { 549 | lr_mult: 2 550 | decay_mult: 0 551 | } 552 | convolution_param { 553 | num_output: 64 554 | kernel_size: 1 555 | weight_filler { 556 | type: "xavier" 557 | std: 0.1 558 | } 559 | bias_filler { 560 | type: "constant" 561 | value: 0.2 562 | } 563 | } 564 | } 565 | layer { 566 | name: "inception_3b/relu_pool_proj" 567 | type: "ReLU" 568 | bottom: "inception_3b/pool_proj" 569 | top: "inception_3b/pool_proj" 570 | } 571 | layer { 572 | name: "inception_3b/output" 573 | type: "Concat" 574 | bottom: "inception_3b/1x1" 575 | bottom: "inception_3b/3x3" 576 | bottom: "inception_3b/5x5" 577 | bottom: "inception_3b/pool_proj" 578 | top: "inception_3b/output" 579 | } 580 | layer { 581 | name: "pool3/3x3_s2" 582 | type: "Pooling" 583 | bottom: "inception_3b/output" 584 | top: "pool3/3x3_s2" 585 | pooling_param { 586 | pool: MAX 587 | kernel_size: 3 588 | stride: 2 589 | } 590 | } 591 | layer { 592 | name: "inception_4a/1x1" 593 | type: "Convolution" 594 | bottom: "pool3/3x3_s2" 595 | top: "inception_4a/1x1" 596 | param { 597 | lr_mult: 1 598 | decay_mult: 1 599 | } 600 | param { 601 | lr_mult: 2 602 | decay_mult: 0 603 | } 604 | convolution_param { 605 | num_output: 192 606 | kernel_size: 1 607 | weight_filler { 608 | type: "xavier" 609 | std: 0.03 610 | } 611 | bias_filler { 612 | type: "constant" 613 | value: 0.2 614 | } 615 | } 616 | } 617 | layer { 618 | name: "inception_4a/relu_1x1" 619 | type: "ReLU" 620 | bottom: "inception_4a/1x1" 621 | top: "inception_4a/1x1" 622 | } 623 | layer { 624 | name: "inception_4a/3x3_reduce" 625 | type: "Convolution" 626 | bottom: "pool3/3x3_s2" 627 | top: "inception_4a/3x3_reduce" 628 | param { 629 | lr_mult: 1 630 | decay_mult: 1 631 | } 632 | param { 633 | lr_mult: 2 634 | decay_mult: 0 635 | } 636 | convolution_param { 637 | num_output: 96 638 | kernel_size: 1 639 | weight_filler { 640 | type: "xavier" 641 | std: 0.09 642 | } 643 | bias_filler { 644 | type: "constant" 645 | value: 0.2 646 | } 647 | } 648 | } 649 | layer { 650 | name: "inception_4a/relu_3x3_reduce" 651 | type: "ReLU" 652 | bottom: "inception_4a/3x3_reduce" 653 | top: "inception_4a/3x3_reduce" 654 | } 655 | layer { 656 | name: "inception_4a/3x3" 657 | type: "Convolution" 658 | bottom: "inception_4a/3x3_reduce" 659 | top: "inception_4a/3x3" 660 | param { 661 | lr_mult: 1 662 | decay_mult: 1 663 | } 664 | param { 665 | lr_mult: 2 666 | decay_mult: 0 667 | } 668 | convolution_param { 669 | num_output: 208 670 | pad: 1 671 | kernel_size: 3 672 | weight_filler { 673 | type: "xavier" 674 | std: 0.03 675 | } 676 | bias_filler { 677 | type: "constant" 678 | value: 0.2 679 | } 680 | } 681 | } 682 | layer { 683 | name: "inception_4a/relu_3x3" 684 | type: "ReLU" 685 | bottom: "inception_4a/3x3" 686 | top: "inception_4a/3x3" 687 | } 688 | layer { 689 | name: "inception_4a/5x5_reduce" 690 | type: "Convolution" 691 | bottom: "pool3/3x3_s2" 692 | top: "inception_4a/5x5_reduce" 693 | param { 694 | lr_mult: 1 695 | decay_mult: 1 696 | } 697 | param { 698 | lr_mult: 2 699 | decay_mult: 0 700 | } 701 | convolution_param { 702 | num_output: 16 703 | kernel_size: 1 704 | weight_filler { 705 | type: "xavier" 706 | std: 0.2 707 | } 708 | bias_filler { 709 | type: "constant" 710 | value: 0.2 711 | } 712 | } 713 | } 714 | layer { 715 | name: "inception_4a/relu_5x5_reduce" 716 | type: "ReLU" 717 | bottom: "inception_4a/5x5_reduce" 718 | top: "inception_4a/5x5_reduce" 719 | } 720 | layer { 721 | name: "inception_4a/5x5" 722 | type: "Convolution" 723 | bottom: "inception_4a/5x5_reduce" 724 | top: "inception_4a/5x5" 725 | param { 726 | lr_mult: 1 727 | decay_mult: 1 728 | } 729 | param { 730 | lr_mult: 2 731 | decay_mult: 0 732 | } 733 | convolution_param { 734 | num_output: 48 735 | pad: 2 736 | kernel_size: 5 737 | weight_filler { 738 | type: "xavier" 739 | std: 0.03 740 | } 741 | bias_filler { 742 | type: "constant" 743 | value: 0.2 744 | } 745 | } 746 | } 747 | layer { 748 | name: "inception_4a/relu_5x5" 749 | type: "ReLU" 750 | bottom: "inception_4a/5x5" 751 | top: "inception_4a/5x5" 752 | } 753 | layer { 754 | name: "inception_4a/pool" 755 | type: "Pooling" 756 | bottom: "pool3/3x3_s2" 757 | top: "inception_4a/pool" 758 | pooling_param { 759 | pool: MAX 760 | kernel_size: 3 761 | stride: 1 762 | pad: 1 763 | } 764 | } 765 | layer { 766 | name: "inception_4a/pool_proj" 767 | type: "Convolution" 768 | bottom: "inception_4a/pool" 769 | top: "inception_4a/pool_proj" 770 | param { 771 | lr_mult: 1 772 | decay_mult: 1 773 | } 774 | param { 775 | lr_mult: 2 776 | decay_mult: 0 777 | } 778 | convolution_param { 779 | num_output: 64 780 | kernel_size: 1 781 | weight_filler { 782 | type: "xavier" 783 | std: 0.1 784 | } 785 | bias_filler { 786 | type: "constant" 787 | value: 0.2 788 | } 789 | } 790 | } 791 | layer { 792 | name: "inception_4a/relu_pool_proj" 793 | type: "ReLU" 794 | bottom: "inception_4a/pool_proj" 795 | top: "inception_4a/pool_proj" 796 | } 797 | layer { 798 | name: "inception_4a/output" 799 | type: "Concat" 800 | bottom: "inception_4a/1x1" 801 | bottom: "inception_4a/3x3" 802 | bottom: "inception_4a/5x5" 803 | bottom: "inception_4a/pool_proj" 804 | top: "inception_4a/output" 805 | } 806 | layer { 807 | name: "inception_4b/1x1" 808 | type: "Convolution" 809 | bottom: "inception_4a/output" 810 | top: "inception_4b/1x1" 811 | param { 812 | lr_mult: 1 813 | decay_mult: 1 814 | } 815 | param { 816 | lr_mult: 2 817 | decay_mult: 0 818 | } 819 | convolution_param { 820 | num_output: 160 821 | kernel_size: 1 822 | weight_filler { 823 | type: "xavier" 824 | std: 0.03 825 | } 826 | bias_filler { 827 | type: "constant" 828 | value: 0.2 829 | } 830 | } 831 | } 832 | layer { 833 | name: "inception_4b/relu_1x1" 834 | type: "ReLU" 835 | bottom: "inception_4b/1x1" 836 | top: "inception_4b/1x1" 837 | } 838 | layer { 839 | name: "inception_4b/3x3_reduce" 840 | type: "Convolution" 841 | bottom: "inception_4a/output" 842 | top: "inception_4b/3x3_reduce" 843 | param { 844 | lr_mult: 1 845 | decay_mult: 1 846 | } 847 | param { 848 | lr_mult: 2 849 | decay_mult: 0 850 | } 851 | convolution_param { 852 | num_output: 112 853 | kernel_size: 1 854 | weight_filler { 855 | type: "xavier" 856 | std: 0.09 857 | } 858 | bias_filler { 859 | type: "constant" 860 | value: 0.2 861 | } 862 | } 863 | } 864 | layer { 865 | name: "inception_4b/relu_3x3_reduce" 866 | type: "ReLU" 867 | bottom: "inception_4b/3x3_reduce" 868 | top: "inception_4b/3x3_reduce" 869 | } 870 | layer { 871 | name: "inception_4b/3x3" 872 | type: "Convolution" 873 | bottom: "inception_4b/3x3_reduce" 874 | top: "inception_4b/3x3" 875 | param { 876 | lr_mult: 1 877 | decay_mult: 1 878 | } 879 | param { 880 | lr_mult: 2 881 | decay_mult: 0 882 | } 883 | convolution_param { 884 | num_output: 224 885 | pad: 1 886 | kernel_size: 3 887 | weight_filler { 888 | type: "xavier" 889 | std: 0.03 890 | } 891 | bias_filler { 892 | type: "constant" 893 | value: 0.2 894 | } 895 | } 896 | } 897 | layer { 898 | name: "inception_4b/relu_3x3" 899 | type: "ReLU" 900 | bottom: "inception_4b/3x3" 901 | top: "inception_4b/3x3" 902 | } 903 | layer { 904 | name: "inception_4b/5x5_reduce" 905 | type: "Convolution" 906 | bottom: "inception_4a/output" 907 | top: "inception_4b/5x5_reduce" 908 | param { 909 | lr_mult: 1 910 | decay_mult: 1 911 | } 912 | param { 913 | lr_mult: 2 914 | decay_mult: 0 915 | } 916 | convolution_param { 917 | num_output: 24 918 | kernel_size: 1 919 | weight_filler { 920 | type: "xavier" 921 | std: 0.2 922 | } 923 | bias_filler { 924 | type: "constant" 925 | value: 0.2 926 | } 927 | } 928 | } 929 | layer { 930 | name: "inception_4b/relu_5x5_reduce" 931 | type: "ReLU" 932 | bottom: "inception_4b/5x5_reduce" 933 | top: "inception_4b/5x5_reduce" 934 | } 935 | layer { 936 | name: "inception_4b/5x5" 937 | type: "Convolution" 938 | bottom: "inception_4b/5x5_reduce" 939 | top: "inception_4b/5x5" 940 | param { 941 | lr_mult: 1 942 | decay_mult: 1 943 | } 944 | param { 945 | lr_mult: 2 946 | decay_mult: 0 947 | } 948 | convolution_param { 949 | num_output: 64 950 | pad: 2 951 | kernel_size: 5 952 | weight_filler { 953 | type: "xavier" 954 | std: 0.03 955 | } 956 | bias_filler { 957 | type: "constant" 958 | value: 0.2 959 | } 960 | } 961 | } 962 | layer { 963 | name: "inception_4b/relu_5x5" 964 | type: "ReLU" 965 | bottom: "inception_4b/5x5" 966 | top: "inception_4b/5x5" 967 | } 968 | layer { 969 | name: "inception_4b/pool" 970 | type: "Pooling" 971 | bottom: "inception_4a/output" 972 | top: "inception_4b/pool" 973 | pooling_param { 974 | pool: MAX 975 | kernel_size: 3 976 | stride: 1 977 | pad: 1 978 | } 979 | } 980 | layer { 981 | name: "inception_4b/pool_proj" 982 | type: "Convolution" 983 | bottom: "inception_4b/pool" 984 | top: "inception_4b/pool_proj" 985 | param { 986 | lr_mult: 1 987 | decay_mult: 1 988 | } 989 | param { 990 | lr_mult: 2 991 | decay_mult: 0 992 | } 993 | convolution_param { 994 | num_output: 64 995 | kernel_size: 1 996 | weight_filler { 997 | type: "xavier" 998 | std: 0.1 999 | } 1000 | bias_filler { 1001 | type: "constant" 1002 | value: 0.2 1003 | } 1004 | } 1005 | } 1006 | layer { 1007 | name: "inception_4b/relu_pool_proj" 1008 | type: "ReLU" 1009 | bottom: "inception_4b/pool_proj" 1010 | top: "inception_4b/pool_proj" 1011 | } 1012 | layer { 1013 | name: "inception_4b/output" 1014 | type: "Concat" 1015 | bottom: "inception_4b/1x1" 1016 | bottom: "inception_4b/3x3" 1017 | bottom: "inception_4b/5x5" 1018 | bottom: "inception_4b/pool_proj" 1019 | top: "inception_4b/output" 1020 | } 1021 | layer { 1022 | name: "inception_4c/1x1" 1023 | type: "Convolution" 1024 | bottom: "inception_4b/output" 1025 | top: "inception_4c/1x1" 1026 | param { 1027 | lr_mult: 1 1028 | decay_mult: 1 1029 | } 1030 | param { 1031 | lr_mult: 2 1032 | decay_mult: 0 1033 | } 1034 | convolution_param { 1035 | num_output: 128 1036 | kernel_size: 1 1037 | weight_filler { 1038 | type: "xavier" 1039 | std: 0.03 1040 | } 1041 | bias_filler { 1042 | type: "constant" 1043 | value: 0.2 1044 | } 1045 | } 1046 | } 1047 | layer { 1048 | name: "inception_4c/relu_1x1" 1049 | type: "ReLU" 1050 | bottom: "inception_4c/1x1" 1051 | top: "inception_4c/1x1" 1052 | } 1053 | layer { 1054 | name: "inception_4c/3x3_reduce" 1055 | type: "Convolution" 1056 | bottom: "inception_4b/output" 1057 | top: "inception_4c/3x3_reduce" 1058 | param { 1059 | lr_mult: 1 1060 | decay_mult: 1 1061 | } 1062 | param { 1063 | lr_mult: 2 1064 | decay_mult: 0 1065 | } 1066 | convolution_param { 1067 | num_output: 128 1068 | kernel_size: 1 1069 | weight_filler { 1070 | type: "xavier" 1071 | std: 0.09 1072 | } 1073 | bias_filler { 1074 | type: "constant" 1075 | value: 0.2 1076 | } 1077 | } 1078 | } 1079 | layer { 1080 | name: "inception_4c/relu_3x3_reduce" 1081 | type: "ReLU" 1082 | bottom: "inception_4c/3x3_reduce" 1083 | top: "inception_4c/3x3_reduce" 1084 | } 1085 | layer { 1086 | name: "inception_4c/3x3" 1087 | type: "Convolution" 1088 | bottom: "inception_4c/3x3_reduce" 1089 | top: "inception_4c/3x3" 1090 | param { 1091 | lr_mult: 1 1092 | decay_mult: 1 1093 | } 1094 | param { 1095 | lr_mult: 2 1096 | decay_mult: 0 1097 | } 1098 | convolution_param { 1099 | num_output: 256 1100 | pad: 1 1101 | kernel_size: 3 1102 | weight_filler { 1103 | type: "xavier" 1104 | std: 0.03 1105 | } 1106 | bias_filler { 1107 | type: "constant" 1108 | value: 0.2 1109 | } 1110 | } 1111 | } 1112 | layer { 1113 | name: "inception_4c/relu_3x3" 1114 | type: "ReLU" 1115 | bottom: "inception_4c/3x3" 1116 | top: "inception_4c/3x3" 1117 | } 1118 | layer { 1119 | name: "inception_4c/5x5_reduce" 1120 | type: "Convolution" 1121 | bottom: "inception_4b/output" 1122 | top: "inception_4c/5x5_reduce" 1123 | param { 1124 | lr_mult: 1 1125 | decay_mult: 1 1126 | } 1127 | param { 1128 | lr_mult: 2 1129 | decay_mult: 0 1130 | } 1131 | convolution_param { 1132 | num_output: 24 1133 | kernel_size: 1 1134 | weight_filler { 1135 | type: "xavier" 1136 | std: 0.2 1137 | } 1138 | bias_filler { 1139 | type: "constant" 1140 | value: 0.2 1141 | } 1142 | } 1143 | } 1144 | layer { 1145 | name: "inception_4c/relu_5x5_reduce" 1146 | type: "ReLU" 1147 | bottom: "inception_4c/5x5_reduce" 1148 | top: "inception_4c/5x5_reduce" 1149 | } 1150 | layer { 1151 | name: "inception_4c/5x5" 1152 | type: "Convolution" 1153 | bottom: "inception_4c/5x5_reduce" 1154 | top: "inception_4c/5x5" 1155 | param { 1156 | lr_mult: 1 1157 | decay_mult: 1 1158 | } 1159 | param { 1160 | lr_mult: 2 1161 | decay_mult: 0 1162 | } 1163 | convolution_param { 1164 | num_output: 64 1165 | pad: 2 1166 | kernel_size: 5 1167 | weight_filler { 1168 | type: "xavier" 1169 | std: 0.03 1170 | } 1171 | bias_filler { 1172 | type: "constant" 1173 | value: 0.2 1174 | } 1175 | } 1176 | } 1177 | layer { 1178 | name: "inception_4c/relu_5x5" 1179 | type: "ReLU" 1180 | bottom: "inception_4c/5x5" 1181 | top: "inception_4c/5x5" 1182 | } 1183 | layer { 1184 | name: "inception_4c/pool" 1185 | type: "Pooling" 1186 | bottom: "inception_4b/output" 1187 | top: "inception_4c/pool" 1188 | pooling_param { 1189 | pool: MAX 1190 | kernel_size: 3 1191 | stride: 1 1192 | pad: 1 1193 | } 1194 | } 1195 | layer { 1196 | name: "inception_4c/pool_proj" 1197 | type: "Convolution" 1198 | bottom: "inception_4c/pool" 1199 | top: "inception_4c/pool_proj" 1200 | param { 1201 | lr_mult: 1 1202 | decay_mult: 1 1203 | } 1204 | param { 1205 | lr_mult: 2 1206 | decay_mult: 0 1207 | } 1208 | convolution_param { 1209 | num_output: 64 1210 | kernel_size: 1 1211 | weight_filler { 1212 | type: "xavier" 1213 | std: 0.1 1214 | } 1215 | bias_filler { 1216 | type: "constant" 1217 | value: 0.2 1218 | } 1219 | } 1220 | } 1221 | layer { 1222 | name: "inception_4c/relu_pool_proj" 1223 | type: "ReLU" 1224 | bottom: "inception_4c/pool_proj" 1225 | top: "inception_4c/pool_proj" 1226 | } 1227 | layer { 1228 | name: "inception_4c/output" 1229 | type: "Concat" 1230 | bottom: "inception_4c/1x1" 1231 | bottom: "inception_4c/3x3" 1232 | bottom: "inception_4c/5x5" 1233 | bottom: "inception_4c/pool_proj" 1234 | top: "inception_4c/output" 1235 | } 1236 | layer { 1237 | name: "inception_4d/1x1" 1238 | type: "Convolution" 1239 | bottom: "inception_4c/output" 1240 | top: "inception_4d/1x1" 1241 | param { 1242 | lr_mult: 1 1243 | decay_mult: 1 1244 | } 1245 | param { 1246 | lr_mult: 2 1247 | decay_mult: 0 1248 | } 1249 | convolution_param { 1250 | num_output: 112 1251 | kernel_size: 1 1252 | weight_filler { 1253 | type: "xavier" 1254 | std: 0.03 1255 | } 1256 | bias_filler { 1257 | type: "constant" 1258 | value: 0.2 1259 | } 1260 | } 1261 | } 1262 | layer { 1263 | name: "inception_4d/relu_1x1" 1264 | type: "ReLU" 1265 | bottom: "inception_4d/1x1" 1266 | top: "inception_4d/1x1" 1267 | } 1268 | layer { 1269 | name: "inception_4d/3x3_reduce" 1270 | type: "Convolution" 1271 | bottom: "inception_4c/output" 1272 | top: "inception_4d/3x3_reduce" 1273 | param { 1274 | lr_mult: 1 1275 | decay_mult: 1 1276 | } 1277 | param { 1278 | lr_mult: 2 1279 | decay_mult: 0 1280 | } 1281 | convolution_param { 1282 | num_output: 144 1283 | kernel_size: 1 1284 | weight_filler { 1285 | type: "xavier" 1286 | std: 0.09 1287 | } 1288 | bias_filler { 1289 | type: "constant" 1290 | value: 0.2 1291 | } 1292 | } 1293 | } 1294 | layer { 1295 | name: "inception_4d/relu_3x3_reduce" 1296 | type: "ReLU" 1297 | bottom: "inception_4d/3x3_reduce" 1298 | top: "inception_4d/3x3_reduce" 1299 | } 1300 | layer { 1301 | name: "inception_4d/3x3" 1302 | type: "Convolution" 1303 | bottom: "inception_4d/3x3_reduce" 1304 | top: "inception_4d/3x3" 1305 | param { 1306 | lr_mult: 1 1307 | decay_mult: 1 1308 | } 1309 | param { 1310 | lr_mult: 2 1311 | decay_mult: 0 1312 | } 1313 | convolution_param { 1314 | num_output: 288 1315 | pad: 1 1316 | kernel_size: 3 1317 | weight_filler { 1318 | type: "xavier" 1319 | std: 0.03 1320 | } 1321 | bias_filler { 1322 | type: "constant" 1323 | value: 0.2 1324 | } 1325 | } 1326 | } 1327 | layer { 1328 | name: "inception_4d/relu_3x3" 1329 | type: "ReLU" 1330 | bottom: "inception_4d/3x3" 1331 | top: "inception_4d/3x3" 1332 | } 1333 | layer { 1334 | name: "inception_4d/5x5_reduce" 1335 | type: "Convolution" 1336 | bottom: "inception_4c/output" 1337 | top: "inception_4d/5x5_reduce" 1338 | param { 1339 | lr_mult: 1 1340 | decay_mult: 1 1341 | } 1342 | param { 1343 | lr_mult: 2 1344 | decay_mult: 0 1345 | } 1346 | convolution_param { 1347 | num_output: 32 1348 | kernel_size: 1 1349 | weight_filler { 1350 | type: "xavier" 1351 | std: 0.2 1352 | } 1353 | bias_filler { 1354 | type: "constant" 1355 | value: 0.2 1356 | } 1357 | } 1358 | } 1359 | layer { 1360 | name: "inception_4d/relu_5x5_reduce" 1361 | type: "ReLU" 1362 | bottom: "inception_4d/5x5_reduce" 1363 | top: "inception_4d/5x5_reduce" 1364 | } 1365 | layer { 1366 | name: "inception_4d/5x5" 1367 | type: "Convolution" 1368 | bottom: "inception_4d/5x5_reduce" 1369 | top: "inception_4d/5x5" 1370 | param { 1371 | lr_mult: 1 1372 | decay_mult: 1 1373 | } 1374 | param { 1375 | lr_mult: 2 1376 | decay_mult: 0 1377 | } 1378 | convolution_param { 1379 | num_output: 64 1380 | pad: 2 1381 | kernel_size: 5 1382 | weight_filler { 1383 | type: "xavier" 1384 | std: 0.03 1385 | } 1386 | bias_filler { 1387 | type: "constant" 1388 | value: 0.2 1389 | } 1390 | } 1391 | } 1392 | layer { 1393 | name: "inception_4d/relu_5x5" 1394 | type: "ReLU" 1395 | bottom: "inception_4d/5x5" 1396 | top: "inception_4d/5x5" 1397 | } 1398 | layer { 1399 | name: "inception_4d/pool" 1400 | type: "Pooling" 1401 | bottom: "inception_4c/output" 1402 | top: "inception_4d/pool" 1403 | pooling_param { 1404 | pool: MAX 1405 | kernel_size: 3 1406 | stride: 1 1407 | pad: 1 1408 | } 1409 | } 1410 | layer { 1411 | name: "inception_4d/pool_proj" 1412 | type: "Convolution" 1413 | bottom: "inception_4d/pool" 1414 | top: "inception_4d/pool_proj" 1415 | param { 1416 | lr_mult: 1 1417 | decay_mult: 1 1418 | } 1419 | param { 1420 | lr_mult: 2 1421 | decay_mult: 0 1422 | } 1423 | convolution_param { 1424 | num_output: 64 1425 | kernel_size: 1 1426 | weight_filler { 1427 | type: "xavier" 1428 | std: 0.1 1429 | } 1430 | bias_filler { 1431 | type: "constant" 1432 | value: 0.2 1433 | } 1434 | } 1435 | } 1436 | layer { 1437 | name: "inception_4d/relu_pool_proj" 1438 | type: "ReLU" 1439 | bottom: "inception_4d/pool_proj" 1440 | top: "inception_4d/pool_proj" 1441 | } 1442 | layer { 1443 | name: "inception_4d/output" 1444 | type: "Concat" 1445 | bottom: "inception_4d/1x1" 1446 | bottom: "inception_4d/3x3" 1447 | bottom: "inception_4d/5x5" 1448 | bottom: "inception_4d/pool_proj" 1449 | top: "inception_4d/output" 1450 | } 1451 | layer { 1452 | name: "inception_4e/1x1" 1453 | type: "Convolution" 1454 | bottom: "inception_4d/output" 1455 | top: "inception_4e/1x1" 1456 | param { 1457 | lr_mult: 1 1458 | decay_mult: 1 1459 | } 1460 | param { 1461 | lr_mult: 2 1462 | decay_mult: 0 1463 | } 1464 | convolution_param { 1465 | num_output: 256 1466 | kernel_size: 1 1467 | weight_filler { 1468 | type: "xavier" 1469 | std: 0.03 1470 | } 1471 | bias_filler { 1472 | type: "constant" 1473 | value: 0.2 1474 | } 1475 | } 1476 | } 1477 | layer { 1478 | name: "inception_4e/relu_1x1" 1479 | type: "ReLU" 1480 | bottom: "inception_4e/1x1" 1481 | top: "inception_4e/1x1" 1482 | } 1483 | layer { 1484 | name: "inception_4e/3x3_reduce" 1485 | type: "Convolution" 1486 | bottom: "inception_4d/output" 1487 | top: "inception_4e/3x3_reduce" 1488 | param { 1489 | lr_mult: 1 1490 | decay_mult: 1 1491 | } 1492 | param { 1493 | lr_mult: 2 1494 | decay_mult: 0 1495 | } 1496 | convolution_param { 1497 | num_output: 160 1498 | kernel_size: 1 1499 | weight_filler { 1500 | type: "xavier" 1501 | std: 0.09 1502 | } 1503 | bias_filler { 1504 | type: "constant" 1505 | value: 0.2 1506 | } 1507 | } 1508 | } 1509 | layer { 1510 | name: "inception_4e/relu_3x3_reduce" 1511 | type: "ReLU" 1512 | bottom: "inception_4e/3x3_reduce" 1513 | top: "inception_4e/3x3_reduce" 1514 | } 1515 | layer { 1516 | name: "inception_4e/3x3" 1517 | type: "Convolution" 1518 | bottom: "inception_4e/3x3_reduce" 1519 | top: "inception_4e/3x3" 1520 | param { 1521 | lr_mult: 1 1522 | decay_mult: 1 1523 | } 1524 | param { 1525 | lr_mult: 2 1526 | decay_mult: 0 1527 | } 1528 | convolution_param { 1529 | num_output: 320 1530 | pad: 1 1531 | kernel_size: 3 1532 | weight_filler { 1533 | type: "xavier" 1534 | std: 0.03 1535 | } 1536 | bias_filler { 1537 | type: "constant" 1538 | value: 0.2 1539 | } 1540 | } 1541 | } 1542 | layer { 1543 | name: "inception_4e/relu_3x3" 1544 | type: "ReLU" 1545 | bottom: "inception_4e/3x3" 1546 | top: "inception_4e/3x3" 1547 | } 1548 | layer { 1549 | name: "inception_4e/5x5_reduce" 1550 | type: "Convolution" 1551 | bottom: "inception_4d/output" 1552 | top: "inception_4e/5x5_reduce" 1553 | param { 1554 | lr_mult: 1 1555 | decay_mult: 1 1556 | } 1557 | param { 1558 | lr_mult: 2 1559 | decay_mult: 0 1560 | } 1561 | convolution_param { 1562 | num_output: 32 1563 | kernel_size: 1 1564 | weight_filler { 1565 | type: "xavier" 1566 | std: 0.2 1567 | } 1568 | bias_filler { 1569 | type: "constant" 1570 | value: 0.2 1571 | } 1572 | } 1573 | } 1574 | layer { 1575 | name: "inception_4e/relu_5x5_reduce" 1576 | type: "ReLU" 1577 | bottom: "inception_4e/5x5_reduce" 1578 | top: "inception_4e/5x5_reduce" 1579 | } 1580 | layer { 1581 | name: "inception_4e/5x5" 1582 | type: "Convolution" 1583 | bottom: "inception_4e/5x5_reduce" 1584 | top: "inception_4e/5x5" 1585 | param { 1586 | lr_mult: 1 1587 | decay_mult: 1 1588 | } 1589 | param { 1590 | lr_mult: 2 1591 | decay_mult: 0 1592 | } 1593 | convolution_param { 1594 | num_output: 128 1595 | pad: 2 1596 | kernel_size: 5 1597 | weight_filler { 1598 | type: "xavier" 1599 | std: 0.03 1600 | } 1601 | bias_filler { 1602 | type: "constant" 1603 | value: 0.2 1604 | } 1605 | } 1606 | } 1607 | layer { 1608 | name: "inception_4e/relu_5x5" 1609 | type: "ReLU" 1610 | bottom: "inception_4e/5x5" 1611 | top: "inception_4e/5x5" 1612 | } 1613 | layer { 1614 | name: "inception_4e/pool" 1615 | type: "Pooling" 1616 | bottom: "inception_4d/output" 1617 | top: "inception_4e/pool" 1618 | pooling_param { 1619 | pool: MAX 1620 | kernel_size: 3 1621 | stride: 1 1622 | pad: 1 1623 | } 1624 | } 1625 | layer { 1626 | name: "inception_4e/pool_proj" 1627 | type: "Convolution" 1628 | bottom: "inception_4e/pool" 1629 | top: "inception_4e/pool_proj" 1630 | param { 1631 | lr_mult: 1 1632 | decay_mult: 1 1633 | } 1634 | param { 1635 | lr_mult: 2 1636 | decay_mult: 0 1637 | } 1638 | convolution_param { 1639 | num_output: 128 1640 | kernel_size: 1 1641 | weight_filler { 1642 | type: "xavier" 1643 | std: 0.1 1644 | } 1645 | bias_filler { 1646 | type: "constant" 1647 | value: 0.2 1648 | } 1649 | } 1650 | } 1651 | layer { 1652 | name: "inception_4e/relu_pool_proj" 1653 | type: "ReLU" 1654 | bottom: "inception_4e/pool_proj" 1655 | top: "inception_4e/pool_proj" 1656 | } 1657 | layer { 1658 | name: "inception_4e/output" 1659 | type: "Concat" 1660 | bottom: "inception_4e/1x1" 1661 | bottom: "inception_4e/3x3" 1662 | bottom: "inception_4e/5x5" 1663 | bottom: "inception_4e/pool_proj" 1664 | top: "inception_4e/output" 1665 | } 1666 | layer { 1667 | name: "pool4/3x3_s2" 1668 | type: "Pooling" 1669 | bottom: "inception_4e/output" 1670 | top: "pool4/3x3_s2" 1671 | pooling_param { 1672 | pool: MAX 1673 | kernel_size: 3 1674 | stride: 2 1675 | } 1676 | } 1677 | layer { 1678 | name: "inception_5a/1x1" 1679 | type: "Convolution" 1680 | bottom: "pool4/3x3_s2" 1681 | top: "inception_5a/1x1" 1682 | param { 1683 | lr_mult: 1 1684 | decay_mult: 1 1685 | } 1686 | param { 1687 | lr_mult: 2 1688 | decay_mult: 0 1689 | } 1690 | convolution_param { 1691 | num_output: 256 1692 | kernel_size: 1 1693 | weight_filler { 1694 | type: "xavier" 1695 | std: 0.03 1696 | } 1697 | bias_filler { 1698 | type: "constant" 1699 | value: 0.2 1700 | } 1701 | } 1702 | } 1703 | layer { 1704 | name: "inception_5a/relu_1x1" 1705 | type: "ReLU" 1706 | bottom: "inception_5a/1x1" 1707 | top: "inception_5a/1x1" 1708 | } 1709 | layer { 1710 | name: "inception_5a/3x3_reduce" 1711 | type: "Convolution" 1712 | bottom: "pool4/3x3_s2" 1713 | top: "inception_5a/3x3_reduce" 1714 | param { 1715 | lr_mult: 1 1716 | decay_mult: 1 1717 | } 1718 | param { 1719 | lr_mult: 2 1720 | decay_mult: 0 1721 | } 1722 | convolution_param { 1723 | num_output: 160 1724 | kernel_size: 1 1725 | weight_filler { 1726 | type: "xavier" 1727 | std: 0.09 1728 | } 1729 | bias_filler { 1730 | type: "constant" 1731 | value: 0.2 1732 | } 1733 | } 1734 | } 1735 | layer { 1736 | name: "inception_5a/relu_3x3_reduce" 1737 | type: "ReLU" 1738 | bottom: "inception_5a/3x3_reduce" 1739 | top: "inception_5a/3x3_reduce" 1740 | } 1741 | layer { 1742 | name: "inception_5a/3x3" 1743 | type: "Convolution" 1744 | bottom: "inception_5a/3x3_reduce" 1745 | top: "inception_5a/3x3" 1746 | param { 1747 | lr_mult: 1 1748 | decay_mult: 1 1749 | } 1750 | param { 1751 | lr_mult: 2 1752 | decay_mult: 0 1753 | } 1754 | convolution_param { 1755 | num_output: 320 1756 | pad: 1 1757 | kernel_size: 3 1758 | weight_filler { 1759 | type: "xavier" 1760 | std: 0.03 1761 | } 1762 | bias_filler { 1763 | type: "constant" 1764 | value: 0.2 1765 | } 1766 | } 1767 | } 1768 | layer { 1769 | name: "inception_5a/relu_3x3" 1770 | type: "ReLU" 1771 | bottom: "inception_5a/3x3" 1772 | top: "inception_5a/3x3" 1773 | } 1774 | layer { 1775 | name: "inception_5a/5x5_reduce" 1776 | type: "Convolution" 1777 | bottom: "pool4/3x3_s2" 1778 | top: "inception_5a/5x5_reduce" 1779 | param { 1780 | lr_mult: 1 1781 | decay_mult: 1 1782 | } 1783 | param { 1784 | lr_mult: 2 1785 | decay_mult: 0 1786 | } 1787 | convolution_param { 1788 | num_output: 32 1789 | kernel_size: 1 1790 | weight_filler { 1791 | type: "xavier" 1792 | std: 0.2 1793 | } 1794 | bias_filler { 1795 | type: "constant" 1796 | value: 0.2 1797 | } 1798 | } 1799 | } 1800 | layer { 1801 | name: "inception_5a/relu_5x5_reduce" 1802 | type: "ReLU" 1803 | bottom: "inception_5a/5x5_reduce" 1804 | top: "inception_5a/5x5_reduce" 1805 | } 1806 | layer { 1807 | name: "inception_5a/5x5" 1808 | type: "Convolution" 1809 | bottom: "inception_5a/5x5_reduce" 1810 | top: "inception_5a/5x5" 1811 | param { 1812 | lr_mult: 1 1813 | decay_mult: 1 1814 | } 1815 | param { 1816 | lr_mult: 2 1817 | decay_mult: 0 1818 | } 1819 | convolution_param { 1820 | num_output: 128 1821 | pad: 2 1822 | kernel_size: 5 1823 | weight_filler { 1824 | type: "xavier" 1825 | std: 0.03 1826 | } 1827 | bias_filler { 1828 | type: "constant" 1829 | value: 0.2 1830 | } 1831 | } 1832 | } 1833 | layer { 1834 | name: "inception_5a/relu_5x5" 1835 | type: "ReLU" 1836 | bottom: "inception_5a/5x5" 1837 | top: "inception_5a/5x5" 1838 | } 1839 | layer { 1840 | name: "inception_5a/pool" 1841 | type: "Pooling" 1842 | bottom: "pool4/3x3_s2" 1843 | top: "inception_5a/pool" 1844 | pooling_param { 1845 | pool: MAX 1846 | kernel_size: 3 1847 | stride: 1 1848 | pad: 1 1849 | } 1850 | } 1851 | layer { 1852 | name: "inception_5a/pool_proj" 1853 | type: "Convolution" 1854 | bottom: "inception_5a/pool" 1855 | top: "inception_5a/pool_proj" 1856 | param { 1857 | lr_mult: 1 1858 | decay_mult: 1 1859 | } 1860 | param { 1861 | lr_mult: 2 1862 | decay_mult: 0 1863 | } 1864 | convolution_param { 1865 | num_output: 128 1866 | kernel_size: 1 1867 | weight_filler { 1868 | type: "xavier" 1869 | std: 0.1 1870 | } 1871 | bias_filler { 1872 | type: "constant" 1873 | value: 0.2 1874 | } 1875 | } 1876 | } 1877 | layer { 1878 | name: "inception_5a/relu_pool_proj" 1879 | type: "ReLU" 1880 | bottom: "inception_5a/pool_proj" 1881 | top: "inception_5a/pool_proj" 1882 | } 1883 | layer { 1884 | name: "inception_5a/output" 1885 | type: "Concat" 1886 | bottom: "inception_5a/1x1" 1887 | bottom: "inception_5a/3x3" 1888 | bottom: "inception_5a/5x5" 1889 | bottom: "inception_5a/pool_proj" 1890 | top: "inception_5a/output" 1891 | } 1892 | layer { 1893 | name: "inception_5b/1x1" 1894 | type: "Convolution" 1895 | bottom: "inception_5a/output" 1896 | top: "inception_5b/1x1" 1897 | param { 1898 | lr_mult: 1 1899 | decay_mult: 1 1900 | } 1901 | param { 1902 | lr_mult: 2 1903 | decay_mult: 0 1904 | } 1905 | convolution_param { 1906 | num_output: 384 1907 | kernel_size: 1 1908 | weight_filler { 1909 | type: "xavier" 1910 | std: 0.03 1911 | } 1912 | bias_filler { 1913 | type: "constant" 1914 | value: 0.2 1915 | } 1916 | } 1917 | } 1918 | layer { 1919 | name: "inception_5b/relu_1x1" 1920 | type: "ReLU" 1921 | bottom: "inception_5b/1x1" 1922 | top: "inception_5b/1x1" 1923 | } 1924 | layer { 1925 | name: "inception_5b/3x3_reduce" 1926 | type: "Convolution" 1927 | bottom: "inception_5a/output" 1928 | top: "inception_5b/3x3_reduce" 1929 | param { 1930 | lr_mult: 1 1931 | decay_mult: 1 1932 | } 1933 | param { 1934 | lr_mult: 2 1935 | decay_mult: 0 1936 | } 1937 | convolution_param { 1938 | num_output: 192 1939 | kernel_size: 1 1940 | weight_filler { 1941 | type: "xavier" 1942 | std: 0.09 1943 | } 1944 | bias_filler { 1945 | type: "constant" 1946 | value: 0.2 1947 | } 1948 | } 1949 | } 1950 | layer { 1951 | name: "inception_5b/relu_3x3_reduce" 1952 | type: "ReLU" 1953 | bottom: "inception_5b/3x3_reduce" 1954 | top: "inception_5b/3x3_reduce" 1955 | } 1956 | layer { 1957 | name: "inception_5b/3x3" 1958 | type: "Convolution" 1959 | bottom: "inception_5b/3x3_reduce" 1960 | top: "inception_5b/3x3" 1961 | param { 1962 | lr_mult: 1 1963 | decay_mult: 1 1964 | } 1965 | param { 1966 | lr_mult: 2 1967 | decay_mult: 0 1968 | } 1969 | convolution_param { 1970 | num_output: 384 1971 | pad: 1 1972 | kernel_size: 3 1973 | weight_filler { 1974 | type: "xavier" 1975 | std: 0.03 1976 | } 1977 | bias_filler { 1978 | type: "constant" 1979 | value: 0.2 1980 | } 1981 | } 1982 | } 1983 | layer { 1984 | name: "inception_5b/relu_3x3" 1985 | type: "ReLU" 1986 | bottom: "inception_5b/3x3" 1987 | top: "inception_5b/3x3" 1988 | } 1989 | layer { 1990 | name: "inception_5b/5x5_reduce" 1991 | type: "Convolution" 1992 | bottom: "inception_5a/output" 1993 | top: "inception_5b/5x5_reduce" 1994 | param { 1995 | lr_mult: 1 1996 | decay_mult: 1 1997 | } 1998 | param { 1999 | lr_mult: 2 2000 | decay_mult: 0 2001 | } 2002 | convolution_param { 2003 | num_output: 48 2004 | kernel_size: 1 2005 | weight_filler { 2006 | type: "xavier" 2007 | std: 0.2 2008 | } 2009 | bias_filler { 2010 | type: "constant" 2011 | value: 0.2 2012 | } 2013 | } 2014 | } 2015 | layer { 2016 | name: "inception_5b/relu_5x5_reduce" 2017 | type: "ReLU" 2018 | bottom: "inception_5b/5x5_reduce" 2019 | top: "inception_5b/5x5_reduce" 2020 | } 2021 | layer { 2022 | name: "inception_5b/5x5" 2023 | type: "Convolution" 2024 | bottom: "inception_5b/5x5_reduce" 2025 | top: "inception_5b/5x5" 2026 | param { 2027 | lr_mult: 1 2028 | decay_mult: 1 2029 | } 2030 | param { 2031 | lr_mult: 2 2032 | decay_mult: 0 2033 | } 2034 | convolution_param { 2035 | num_output: 128 2036 | pad: 2 2037 | kernel_size: 5 2038 | weight_filler { 2039 | type: "xavier" 2040 | std: 0.03 2041 | } 2042 | bias_filler { 2043 | type: "constant" 2044 | value: 0.2 2045 | } 2046 | } 2047 | } 2048 | layer { 2049 | name: "inception_5b/relu_5x5" 2050 | type: "ReLU" 2051 | bottom: "inception_5b/5x5" 2052 | top: "inception_5b/5x5" 2053 | } 2054 | layer { 2055 | name: "inception_5b/pool" 2056 | type: "Pooling" 2057 | bottom: "inception_5a/output" 2058 | top: "inception_5b/pool" 2059 | pooling_param { 2060 | pool: MAX 2061 | kernel_size: 3 2062 | stride: 1 2063 | pad: 1 2064 | } 2065 | } 2066 | layer { 2067 | name: "inception_5b/pool_proj" 2068 | type: "Convolution" 2069 | bottom: "inception_5b/pool" 2070 | top: "inception_5b/pool_proj" 2071 | param { 2072 | lr_mult: 1 2073 | decay_mult: 1 2074 | } 2075 | param { 2076 | lr_mult: 2 2077 | decay_mult: 0 2078 | } 2079 | convolution_param { 2080 | num_output: 128 2081 | kernel_size: 1 2082 | weight_filler { 2083 | type: "xavier" 2084 | std: 0.1 2085 | } 2086 | bias_filler { 2087 | type: "constant" 2088 | value: 0.2 2089 | } 2090 | } 2091 | } 2092 | layer { 2093 | name: "inception_5b/relu_pool_proj" 2094 | type: "ReLU" 2095 | bottom: "inception_5b/pool_proj" 2096 | top: "inception_5b/pool_proj" 2097 | } 2098 | layer { 2099 | name: "inception_5b/output" 2100 | type: "Concat" 2101 | bottom: "inception_5b/1x1" 2102 | bottom: "inception_5b/3x3" 2103 | bottom: "inception_5b/5x5" 2104 | bottom: "inception_5b/pool_proj" 2105 | top: "inception_5b/output" 2106 | } 2107 | layer { 2108 | name: "pool5/7x7_s1" 2109 | type: "Pooling" 2110 | bottom: "inception_5b/output" 2111 | top: "pool5/7x7_s1" 2112 | pooling_param { 2113 | pool: AVE 2114 | kernel_size: 7 2115 | stride: 1 2116 | } 2117 | } 2118 | layer { 2119 | name: "pool5/drop_7x7_s1" 2120 | type: "Dropout" 2121 | bottom: "pool5/7x7_s1" 2122 | top: "pool5/7x7_s1" 2123 | dropout_param { 2124 | dropout_ratio: 0.4 2125 | } 2126 | } 2127 | layer { 2128 | name: "loss3/classifier" 2129 | type: "InnerProduct" 2130 | bottom: "pool5/7x7_s1" 2131 | top: "loss3/classifier" 2132 | param { 2133 | lr_mult: 1 2134 | decay_mult: 1 2135 | } 2136 | param { 2137 | lr_mult: 2 2138 | decay_mult: 0 2139 | } 2140 | inner_product_param { 2141 | num_output: 1000 2142 | weight_filler { 2143 | type: "xavier" 2144 | } 2145 | bias_filler { 2146 | type: "constant" 2147 | value: 0 2148 | } 2149 | } 2150 | } 2151 | layer { 2152 | name: "prob" 2153 | type: "Softmax" 2154 | bottom: "loss3/classifier" 2155 | top: "prob" 2156 | } -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/dcks.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback/dcks.mp4 -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/optFlowFarneback.vcxproj: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | Debug 6 | Win32 7 | 8 | 9 | Release 10 | Win32 11 | 12 | 13 | Debug 14 | x64 15 | 16 | 17 | Release 18 | x64 19 | 20 | 21 | 22 | {F58B092C-1690-4609-AF57-401FEE939829} 23 | Win32Proj 24 | optFlowFarneback 25 | 8.1 26 | 27 | 28 | 29 | Application 30 | true 31 | v140 32 | Unicode 33 | 34 | 35 | Application 36 | false 37 | v140 38 | true 39 | Unicode 40 | 41 | 42 | Application 43 | true 44 | v120 45 | Unicode 46 | 47 | 48 | Application 49 | false 50 | v140 51 | true 52 | Unicode 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | true 74 | 75 | 76 | true 77 | 78 | 79 | false 80 | 81 | 82 | false 83 | 84 | 85 | 86 | 87 | 88 | Level3 89 | Disabled 90 | WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) 91 | true 92 | 93 | 94 | Console 95 | true 96 | 97 | 98 | 99 | 100 | 101 | 102 | Level3 103 | Disabled 104 | _DEBUG;_CONSOLE;%(PreprocessorDefinitions) 105 | true 106 | $(OPENCV_CUDA_DIR)\..\..\include 107 | 108 | 109 | Console 110 | true 111 | $(OPENCV_CUDA_DIR)\lib 112 | opencv_core310d.lib;opencv_ts310d.lib;opencv_optflow310d.lib;opencv_video310d.lib;opencv_videoio310d.lib;opencv_tracking310d.lib;opencv_flann310d.lib;opencv_highgui310d.lib;opencv_imgproc310d.lib;opencv_imgcodecs310d.lib;opencv_xfeatures2d310d.lib;opencv_features2d310d.lib;opencv_calib3d310d.lib;opencv_cudafilters310d.lib;opencv_cudaimgproc310d.lib;opencv_cudastereo310d.lib;opencv_cudaarithm310d.lib;opencv_cudaoptflow310d.lib;%(AdditionalDependencies) 113 | 114 | 115 | 116 | 117 | Level3 118 | 119 | 120 | MaxSpeed 121 | true 122 | true 123 | WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) 124 | true 125 | $(OPENCV_DIR)\..\..\include 126 | 127 | 128 | Console 129 | true 130 | true 131 | true 132 | $(OPENCV_DIR)\lib 133 | opencv_core310.lib;opencv_ts310.lib;opencv_video310.lib;opencv_videoio310.lib;opencv_tracking310.lib;opencv_highgui310.lib;opencv_imgproc310.lib;opencv_imgcodecs310.lib;opencv_features2d310.lib;opencv_calib3d310.lib;opencv_optflow310.lib;opencv_dnn310.lib 134 | 135 | 136 | 137 | 138 | Level3 139 | 140 | 141 | MaxSpeed 142 | true 143 | true 144 | NDEBUG;_CONSOLE;%(PreprocessorDefinitions) 145 | true 146 | $(OPENCV_DIR)\..\..\include 147 | 148 | 149 | Console 150 | true 151 | true 152 | true 153 | $(OPENCV_DIR)\lib 154 | opencv_core310.lib;opencv_ts310.lib;opencv_video310.lib;opencv_videoio310.lib;opencv_tracking310.lib;opencv_highgui310.lib;opencv_imgproc310.lib;opencv_imgcodecs310.lib;opencv_features2d310.lib;opencv_calib3d310.lib;opencv_optflow310.lib;opencv_dnn310.lib 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/optFlowFarneback.vcxproj.filters: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | {4FC737F1-C7A5-4376-A066-2A32D752A2FF} 6 | cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx 7 | 8 | 9 | {93995380-89BD-4b04-88EB-625FBE52EBFB} 10 | h;hh;hpp;hxx;hm;inl;inc;xsd 11 | 12 | 13 | {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} 14 | rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms 15 | 16 | 17 | 18 | 19 | Source Files 20 | 21 | 22 | 23 | 24 | Source Files 25 | 26 | 27 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/readME.txt: -------------------------------------------------------------------------------- 1 | i deleted the following caffemodels from this project (in order to reduce the size of the upload) : 2 | bvlc_googlenet.caffemodel 3 | bvlc_slexnet.caffemodel 4 | 5 | you can download the same from here : 6 | https://github.com/BVLC/caffe/tree/master/models -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/surv4.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback/surv4.mp4 -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/synset_words.txt: -------------------------------------------------------------------------------- 1 | n01440764 tench, Tinca tinca 2 | n01443537 goldfish, Carassius auratus 3 | n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias 4 | n01491361 tiger shark, Galeocerdo cuvieri 5 | n01494475 hammerhead, hammerhead shark 6 | n01496331 electric ray, crampfish, numbfish, torpedo 7 | n01498041 stingray 8 | n01514668 cock 9 | n01514859 hen 10 | n01518878 ostrich, Struthio camelus 11 | n01530575 brambling, Fringilla montifringilla 12 | n01531178 goldfinch, Carduelis carduelis 13 | n01532829 house finch, linnet, Carpodacus mexicanus 14 | n01534433 junco, snowbird 15 | n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea 16 | n01558993 robin, American robin, Turdus migratorius 17 | n01560419 bulbul 18 | n01580077 jay 19 | n01582220 magpie 20 | n01592084 chickadee 21 | n01601694 water ouzel, dipper 22 | n01608432 kite 23 | n01614925 bald eagle, American eagle, Haliaeetus leucocephalus 24 | n01616318 vulture 25 | n01622779 great grey owl, great gray owl, Strix nebulosa 26 | n01629819 European fire salamander, Salamandra salamandra 27 | n01630670 common newt, Triturus vulgaris 28 | n01631663 eft 29 | n01632458 spotted salamander, Ambystoma maculatum 30 | n01632777 axolotl, mud puppy, Ambystoma mexicanum 31 | n01641577 bullfrog, Rana catesbeiana 32 | n01644373 tree frog, tree-frog 33 | n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui 34 | n01664065 loggerhead, loggerhead turtle, Caretta caretta 35 | n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea 36 | n01667114 mud turtle 37 | n01667778 terrapin 38 | n01669191 box turtle, box tortoise 39 | n01675722 banded gecko 40 | n01677366 common iguana, iguana, Iguana iguana 41 | n01682714 American chameleon, anole, Anolis carolinensis 42 | n01685808 whiptail, whiptail lizard 43 | n01687978 agama 44 | n01688243 frilled lizard, Chlamydosaurus kingi 45 | n01689811 alligator lizard 46 | n01692333 Gila monster, Heloderma suspectum 47 | n01693334 green lizard, Lacerta viridis 48 | n01694178 African chameleon, Chamaeleo chamaeleon 49 | n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis 50 | n01697457 African crocodile, Nile crocodile, Crocodylus niloticus 51 | n01698640 American alligator, Alligator mississipiensis 52 | n01704323 triceratops 53 | n01728572 thunder snake, worm snake, Carphophis amoenus 54 | n01728920 ringneck snake, ring-necked snake, ring snake 55 | n01729322 hognose snake, puff adder, sand viper 56 | n01729977 green snake, grass snake 57 | n01734418 king snake, kingsnake 58 | n01735189 garter snake, grass snake 59 | n01737021 water snake 60 | n01739381 vine snake 61 | n01740131 night snake, Hypsiglena torquata 62 | n01742172 boa constrictor, Constrictor constrictor 63 | n01744401 rock python, rock snake, Python sebae 64 | n01748264 Indian cobra, Naja naja 65 | n01749939 green mamba 66 | n01751748 sea snake 67 | n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus 68 | n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus 69 | n01756291 sidewinder, horned rattlesnake, Crotalus cerastes 70 | n01768244 trilobite 71 | n01770081 harvestman, daddy longlegs, Phalangium opilio 72 | n01770393 scorpion 73 | n01773157 black and gold garden spider, Argiope aurantia 74 | n01773549 barn spider, Araneus cavaticus 75 | n01773797 garden spider, Aranea diademata 76 | n01774384 black widow, Latrodectus mactans 77 | n01774750 tarantula 78 | n01775062 wolf spider, hunting spider 79 | n01776313 tick 80 | n01784675 centipede 81 | n01795545 black grouse 82 | n01796340 ptarmigan 83 | n01797886 ruffed grouse, partridge, Bonasa umbellus 84 | n01798484 prairie chicken, prairie grouse, prairie fowl 85 | n01806143 peacock 86 | n01806567 quail 87 | n01807496 partridge 88 | n01817953 African grey, African gray, Psittacus erithacus 89 | n01818515 macaw 90 | n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita 91 | n01820546 lorikeet 92 | n01824575 coucal 93 | n01828970 bee eater 94 | n01829413 hornbill 95 | n01833805 hummingbird 96 | n01843065 jacamar 97 | n01843383 toucan 98 | n01847000 drake 99 | n01855032 red-breasted merganser, Mergus serrator 100 | n01855672 goose 101 | n01860187 black swan, Cygnus atratus 102 | n01871265 tusker 103 | n01872401 echidna, spiny anteater, anteater 104 | n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus 105 | n01877812 wallaby, brush kangaroo 106 | n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus 107 | n01883070 wombat 108 | n01910747 jellyfish 109 | n01914609 sea anemone, anemone 110 | n01917289 brain coral 111 | n01924916 flatworm, platyhelminth 112 | n01930112 nematode, nematode worm, roundworm 113 | n01943899 conch 114 | n01944390 snail 115 | n01945685 slug 116 | n01950731 sea slug, nudibranch 117 | n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore 118 | n01968897 chambered nautilus, pearly nautilus, nautilus 119 | n01978287 Dungeness crab, Cancer magister 120 | n01978455 rock crab, Cancer irroratus 121 | n01980166 fiddler crab 122 | n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica 123 | n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus 124 | n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish 125 | n01985128 crayfish, crawfish, crawdad, crawdaddy 126 | n01986214 hermit crab 127 | n01990800 isopod 128 | n02002556 white stork, Ciconia ciconia 129 | n02002724 black stork, Ciconia nigra 130 | n02006656 spoonbill 131 | n02007558 flamingo 132 | n02009229 little blue heron, Egretta caerulea 133 | n02009912 American egret, great white heron, Egretta albus 134 | n02011460 bittern 135 | n02012849 crane 136 | n02013706 limpkin, Aramus pictus 137 | n02017213 European gallinule, Porphyrio porphyrio 138 | n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana 139 | n02018795 bustard 140 | n02025239 ruddy turnstone, Arenaria interpres 141 | n02027492 red-backed sandpiper, dunlin, Erolia alpina 142 | n02028035 redshank, Tringa totanus 143 | n02033041 dowitcher 144 | n02037110 oystercatcher, oyster catcher 145 | n02051845 pelican 146 | n02056570 king penguin, Aptenodytes patagonica 147 | n02058221 albatross, mollymawk 148 | n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus 149 | n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca 150 | n02074367 dugong, Dugong dugon 151 | n02077923 sea lion 152 | n02085620 Chihuahua 153 | n02085782 Japanese spaniel 154 | n02085936 Maltese dog, Maltese terrier, Maltese 155 | n02086079 Pekinese, Pekingese, Peke 156 | n02086240 Shih-Tzu 157 | n02086646 Blenheim spaniel 158 | n02086910 papillon 159 | n02087046 toy terrier 160 | n02087394 Rhodesian ridgeback 161 | n02088094 Afghan hound, Afghan 162 | n02088238 basset, basset hound 163 | n02088364 beagle 164 | n02088466 bloodhound, sleuthhound 165 | n02088632 bluetick 166 | n02089078 black-and-tan coonhound 167 | n02089867 Walker hound, Walker foxhound 168 | n02089973 English foxhound 169 | n02090379 redbone 170 | n02090622 borzoi, Russian wolfhound 171 | n02090721 Irish wolfhound 172 | n02091032 Italian greyhound 173 | n02091134 whippet 174 | n02091244 Ibizan hound, Ibizan Podenco 175 | n02091467 Norwegian elkhound, elkhound 176 | n02091635 otterhound, otter hound 177 | n02091831 Saluki, gazelle hound 178 | n02092002 Scottish deerhound, deerhound 179 | n02092339 Weimaraner 180 | n02093256 Staffordshire bullterrier, Staffordshire bull terrier 181 | n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier 182 | n02093647 Bedlington terrier 183 | n02093754 Border terrier 184 | n02093859 Kerry blue terrier 185 | n02093991 Irish terrier 186 | n02094114 Norfolk terrier 187 | n02094258 Norwich terrier 188 | n02094433 Yorkshire terrier 189 | n02095314 wire-haired fox terrier 190 | n02095570 Lakeland terrier 191 | n02095889 Sealyham terrier, Sealyham 192 | n02096051 Airedale, Airedale terrier 193 | n02096177 cairn, cairn terrier 194 | n02096294 Australian terrier 195 | n02096437 Dandie Dinmont, Dandie Dinmont terrier 196 | n02096585 Boston bull, Boston terrier 197 | n02097047 miniature schnauzer 198 | n02097130 giant schnauzer 199 | n02097209 standard schnauzer 200 | n02097298 Scotch terrier, Scottish terrier, Scottie 201 | n02097474 Tibetan terrier, chrysanthemum dog 202 | n02097658 silky terrier, Sydney silky 203 | n02098105 soft-coated wheaten terrier 204 | n02098286 West Highland white terrier 205 | n02098413 Lhasa, Lhasa apso 206 | n02099267 flat-coated retriever 207 | n02099429 curly-coated retriever 208 | n02099601 golden retriever 209 | n02099712 Labrador retriever 210 | n02099849 Chesapeake Bay retriever 211 | n02100236 German short-haired pointer 212 | n02100583 vizsla, Hungarian pointer 213 | n02100735 English setter 214 | n02100877 Irish setter, red setter 215 | n02101006 Gordon setter 216 | n02101388 Brittany spaniel 217 | n02101556 clumber, clumber spaniel 218 | n02102040 English springer, English springer spaniel 219 | n02102177 Welsh springer spaniel 220 | n02102318 cocker spaniel, English cocker spaniel, cocker 221 | n02102480 Sussex spaniel 222 | n02102973 Irish water spaniel 223 | n02104029 kuvasz 224 | n02104365 schipperke 225 | n02105056 groenendael 226 | n02105162 malinois 227 | n02105251 briard 228 | n02105412 kelpie 229 | n02105505 komondor 230 | n02105641 Old English sheepdog, bobtail 231 | n02105855 Shetland sheepdog, Shetland sheep dog, Shetland 232 | n02106030 collie 233 | n02106166 Border collie 234 | n02106382 Bouvier des Flandres, Bouviers des Flandres 235 | n02106550 Rottweiler 236 | n02106662 German shepherd, German shepherd dog, German police dog, alsatian 237 | n02107142 Doberman, Doberman pinscher 238 | n02107312 miniature pinscher 239 | n02107574 Greater Swiss Mountain dog 240 | n02107683 Bernese mountain dog 241 | n02107908 Appenzeller 242 | n02108000 EntleBucher 243 | n02108089 boxer 244 | n02108422 bull mastiff 245 | n02108551 Tibetan mastiff 246 | n02108915 French bulldog 247 | n02109047 Great Dane 248 | n02109525 Saint Bernard, St Bernard 249 | n02109961 Eskimo dog, husky 250 | n02110063 malamute, malemute, Alaskan malamute 251 | n02110185 Siberian husky 252 | n02110341 dalmatian, coach dog, carriage dog 253 | n02110627 affenpinscher, monkey pinscher, monkey dog 254 | n02110806 basenji 255 | n02110958 pug, pug-dog 256 | n02111129 Leonberg 257 | n02111277 Newfoundland, Newfoundland dog 258 | n02111500 Great Pyrenees 259 | n02111889 Samoyed, Samoyede 260 | n02112018 Pomeranian 261 | n02112137 chow, chow chow 262 | n02112350 keeshond 263 | n02112706 Brabancon griffon 264 | n02113023 Pembroke, Pembroke Welsh corgi 265 | n02113186 Cardigan, Cardigan Welsh corgi 266 | n02113624 toy poodle 267 | n02113712 miniature poodle 268 | n02113799 standard poodle 269 | n02113978 Mexican hairless 270 | n02114367 timber wolf, grey wolf, gray wolf, Canis lupus 271 | n02114548 white wolf, Arctic wolf, Canis lupus tundrarum 272 | n02114712 red wolf, maned wolf, Canis rufus, Canis niger 273 | n02114855 coyote, prairie wolf, brush wolf, Canis latrans 274 | n02115641 dingo, warrigal, warragal, Canis dingo 275 | n02115913 dhole, Cuon alpinus 276 | n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus 277 | n02117135 hyena, hyaena 278 | n02119022 red fox, Vulpes vulpes 279 | n02119789 kit fox, Vulpes macrotis 280 | n02120079 Arctic fox, white fox, Alopex lagopus 281 | n02120505 grey fox, gray fox, Urocyon cinereoargenteus 282 | n02123045 tabby, tabby cat 283 | n02123159 tiger cat 284 | n02123394 Persian cat 285 | n02123597 Siamese cat, Siamese 286 | n02124075 Egyptian cat 287 | n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor 288 | n02127052 lynx, catamount 289 | n02128385 leopard, Panthera pardus 290 | n02128757 snow leopard, ounce, Panthera uncia 291 | n02128925 jaguar, panther, Panthera onca, Felis onca 292 | n02129165 lion, king of beasts, Panthera leo 293 | n02129604 tiger, Panthera tigris 294 | n02130308 cheetah, chetah, Acinonyx jubatus 295 | n02132136 brown bear, bruin, Ursus arctos 296 | n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus 297 | n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus 298 | n02134418 sloth bear, Melursus ursinus, Ursus ursinus 299 | n02137549 mongoose 300 | n02138441 meerkat, mierkat 301 | n02165105 tiger beetle 302 | n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 303 | n02167151 ground beetle, carabid beetle 304 | n02168699 long-horned beetle, longicorn, longicorn beetle 305 | n02169497 leaf beetle, chrysomelid 306 | n02172182 dung beetle 307 | n02174001 rhinoceros beetle 308 | n02177972 weevil 309 | n02190166 fly 310 | n02206856 bee 311 | n02219486 ant, emmet, pismire 312 | n02226429 grasshopper, hopper 313 | n02229544 cricket 314 | n02231487 walking stick, walkingstick, stick insect 315 | n02233338 cockroach, roach 316 | n02236044 mantis, mantid 317 | n02256656 cicada, cicala 318 | n02259212 leafhopper 319 | n02264363 lacewing, lacewing fly 320 | n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk 321 | n02268853 damselfly 322 | n02276258 admiral 323 | n02277742 ringlet, ringlet butterfly 324 | n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus 325 | n02280649 cabbage butterfly 326 | n02281406 sulphur butterfly, sulfur butterfly 327 | n02281787 lycaenid, lycaenid butterfly 328 | n02317335 starfish, sea star 329 | n02319095 sea urchin 330 | n02321529 sea cucumber, holothurian 331 | n02325366 wood rabbit, cottontail, cottontail rabbit 332 | n02326432 hare 333 | n02328150 Angora, Angora rabbit 334 | n02342885 hamster 335 | n02346627 porcupine, hedgehog 336 | n02356798 fox squirrel, eastern fox squirrel, Sciurus niger 337 | n02361337 marmot 338 | n02363005 beaver 339 | n02364673 guinea pig, Cavia cobaya 340 | n02389026 sorrel 341 | n02391049 zebra 342 | n02395406 hog, pig, grunter, squealer, Sus scrofa 343 | n02396427 wild boar, boar, Sus scrofa 344 | n02397096 warthog 345 | n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius 346 | n02403003 ox 347 | n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis 348 | n02410509 bison 349 | n02412080 ram, tup 350 | n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis 351 | n02417914 ibex, Capra ibex 352 | n02422106 hartebeest 353 | n02422699 impala, Aepyceros melampus 354 | n02423022 gazelle 355 | n02437312 Arabian camel, dromedary, Camelus dromedarius 356 | n02437616 llama 357 | n02441942 weasel 358 | n02442845 mink 359 | n02443114 polecat, fitch, foulmart, foumart, Mustela putorius 360 | n02443484 black-footed ferret, ferret, Mustela nigripes 361 | n02444819 otter 362 | n02445715 skunk, polecat, wood pussy 363 | n02447366 badger 364 | n02454379 armadillo 365 | n02457408 three-toed sloth, ai, Bradypus tridactylus 366 | n02480495 orangutan, orang, orangutang, Pongo pygmaeus 367 | n02480855 gorilla, Gorilla gorilla 368 | n02481823 chimpanzee, chimp, Pan troglodytes 369 | n02483362 gibbon, Hylobates lar 370 | n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus 371 | n02484975 guenon, guenon monkey 372 | n02486261 patas, hussar monkey, Erythrocebus patas 373 | n02486410 baboon 374 | n02487347 macaque 375 | n02488291 langur 376 | n02488702 colobus, colobus monkey 377 | n02489166 proboscis monkey, Nasalis larvatus 378 | n02490219 marmoset 379 | n02492035 capuchin, ringtail, Cebus capucinus 380 | n02492660 howler monkey, howler 381 | n02493509 titi, titi monkey 382 | n02493793 spider monkey, Ateles geoffroyi 383 | n02494079 squirrel monkey, Saimiri sciureus 384 | n02497673 Madagascar cat, ring-tailed lemur, Lemur catta 385 | n02500267 indri, indris, Indri indri, Indri brevicaudatus 386 | n02504013 Indian elephant, Elephas maximus 387 | n02504458 African elephant, Loxodonta africana 388 | n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens 389 | n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 390 | n02514041 barracouta, snoek 391 | n02526121 eel 392 | n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch 393 | n02606052 rock beauty, Holocanthus tricolor 394 | n02607072 anemone fish 395 | n02640242 sturgeon 396 | n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus 397 | n02643566 lionfish 398 | n02655020 puffer, pufferfish, blowfish, globefish 399 | n02666196 abacus 400 | n02667093 abaya 401 | n02669723 academic gown, academic robe, judge's robe 402 | n02672831 accordion, piano accordion, squeeze box 403 | n02676566 acoustic guitar 404 | n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier 405 | n02690373 airliner 406 | n02692877 airship, dirigible 407 | n02699494 altar 408 | n02701002 ambulance 409 | n02704792 amphibian, amphibious vehicle 410 | n02708093 analog clock 411 | n02727426 apiary, bee house 412 | n02730930 apron 413 | n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin 414 | n02749479 assault rifle, assault gun 415 | n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack 416 | n02776631 bakery, bakeshop, bakehouse 417 | n02777292 balance beam, beam 418 | n02782093 balloon 419 | n02783161 ballpoint, ballpoint pen, ballpen, Biro 420 | n02786058 Band Aid 421 | n02787622 banjo 422 | n02788148 bannister, banister, balustrade, balusters, handrail 423 | n02790996 barbell 424 | n02791124 barber chair 425 | n02791270 barbershop 426 | n02793495 barn 427 | n02794156 barometer 428 | n02795169 barrel, cask 429 | n02797295 barrow, garden cart, lawn cart, wheelbarrow 430 | n02799071 baseball 431 | n02802426 basketball 432 | n02804414 bassinet 433 | n02804610 bassoon 434 | n02807133 bathing cap, swimming cap 435 | n02808304 bath towel 436 | n02808440 bathtub, bathing tub, bath, tub 437 | n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 438 | n02814860 beacon, lighthouse, beacon light, pharos 439 | n02815834 beaker 440 | n02817516 bearskin, busby, shako 441 | n02823428 beer bottle 442 | n02823750 beer glass 443 | n02825657 bell cote, bell cot 444 | n02834397 bib 445 | n02835271 bicycle-built-for-two, tandem bicycle, tandem 446 | n02837789 bikini, two-piece 447 | n02840245 binder, ring-binder 448 | n02841315 binoculars, field glasses, opera glasses 449 | n02843684 birdhouse 450 | n02859443 boathouse 451 | n02860847 bobsled, bobsleigh, bob 452 | n02865351 bolo tie, bolo, bola tie, bola 453 | n02869837 bonnet, poke bonnet 454 | n02870880 bookcase 455 | n02871525 bookshop, bookstore, bookstall 456 | n02877765 bottlecap 457 | n02879718 bow 458 | n02883205 bow tie, bow-tie, bowtie 459 | n02892201 brass, memorial tablet, plaque 460 | n02892767 brassiere, bra, bandeau 461 | n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty 462 | n02895154 breastplate, aegis, egis 463 | n02906734 broom 464 | n02909870 bucket, pail 465 | n02910353 buckle 466 | n02916936 bulletproof vest 467 | n02917067 bullet train, bullet 468 | n02927161 butcher shop, meat market 469 | n02930766 cab, hack, taxi, taxicab 470 | n02939185 caldron, cauldron 471 | n02948072 candle, taper, wax light 472 | n02950826 cannon 473 | n02951358 canoe 474 | n02951585 can opener, tin opener 475 | n02963159 cardigan 476 | n02965783 car mirror 477 | n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig 478 | n02966687 carpenter's kit, tool kit 479 | n02971356 carton 480 | n02974003 car wheel 481 | n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM 482 | n02978881 cassette 483 | n02979186 cassette player 484 | n02980441 castle 485 | n02981792 catamaran 486 | n02988304 CD player 487 | n02992211 cello, violoncello 488 | n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone 489 | n02999410 chain 490 | n03000134 chainlink fence 491 | n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour 492 | n03000684 chain saw, chainsaw 493 | n03014705 chest 494 | n03016953 chiffonier, commode 495 | n03017168 chime, bell, gong 496 | n03018349 china cabinet, china closet 497 | n03026506 Christmas stocking 498 | n03028079 church, church building 499 | n03032252 cinema, movie theater, movie theatre, movie house, picture palace 500 | n03041632 cleaver, meat cleaver, chopper 501 | n03042490 cliff dwelling 502 | n03045698 cloak 503 | n03047690 clog, geta, patten, sabot 504 | n03062245 cocktail shaker 505 | n03063599 coffee mug 506 | n03063689 coffeepot 507 | n03065424 coil, spiral, volute, whorl, helix 508 | n03075370 combination lock 509 | n03085013 computer keyboard, keypad 510 | n03089624 confectionery, confectionary, candy store 511 | n03095699 container ship, containership, container vessel 512 | n03100240 convertible 513 | n03109150 corkscrew, bottle screw 514 | n03110669 cornet, horn, trumpet, trump 515 | n03124043 cowboy boot 516 | n03124170 cowboy hat, ten-gallon hat 517 | n03125729 cradle 518 | n03126707 crane 519 | n03127747 crash helmet 520 | n03127925 crate 521 | n03131574 crib, cot 522 | n03133878 Crock Pot 523 | n03134739 croquet ball 524 | n03141823 crutch 525 | n03146219 cuirass 526 | n03160309 dam, dike, dyke 527 | n03179701 desk 528 | n03180011 desktop computer 529 | n03187595 dial telephone, dial phone 530 | n03188531 diaper, nappy, napkin 531 | n03196217 digital clock 532 | n03197337 digital watch 533 | n03201208 dining table, board 534 | n03207743 dishrag, dishcloth 535 | n03207941 dishwasher, dish washer, dishwashing machine 536 | n03208938 disk brake, disc brake 537 | n03216828 dock, dockage, docking facility 538 | n03218198 dogsled, dog sled, dog sleigh 539 | n03220513 dome 540 | n03223299 doormat, welcome mat 541 | n03240683 drilling platform, offshore rig 542 | n03249569 drum, membranophone, tympan 543 | n03250847 drumstick 544 | n03255030 dumbbell 545 | n03259280 Dutch oven 546 | n03271574 electric fan, blower 547 | n03272010 electric guitar 548 | n03272562 electric locomotive 549 | n03290653 entertainment center 550 | n03291819 envelope 551 | n03297495 espresso maker 552 | n03314780 face powder 553 | n03325584 feather boa, boa 554 | n03337140 file, file cabinet, filing cabinet 555 | n03344393 fireboat 556 | n03345487 fire engine, fire truck 557 | n03347037 fire screen, fireguard 558 | n03355925 flagpole, flagstaff 559 | n03372029 flute, transverse flute 560 | n03376595 folding chair 561 | n03379051 football helmet 562 | n03384352 forklift 563 | n03388043 fountain 564 | n03388183 fountain pen 565 | n03388549 four-poster 566 | n03393912 freight car 567 | n03394916 French horn, horn 568 | n03400231 frying pan, frypan, skillet 569 | n03404251 fur coat 570 | n03417042 garbage truck, dustcart 571 | n03424325 gasmask, respirator, gas helmet 572 | n03425413 gas pump, gasoline pump, petrol pump, island dispenser 573 | n03443371 goblet 574 | n03444034 go-kart 575 | n03445777 golf ball 576 | n03445924 golfcart, golf cart 577 | n03447447 gondola 578 | n03447721 gong, tam-tam 579 | n03450230 gown 580 | n03452741 grand piano, grand 581 | n03457902 greenhouse, nursery, glasshouse 582 | n03459775 grille, radiator grille 583 | n03461385 grocery store, grocery, food market, market 584 | n03467068 guillotine 585 | n03476684 hair slide 586 | n03476991 hair spray 587 | n03478589 half track 588 | n03481172 hammer 589 | n03482405 hamper 590 | n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier 591 | n03485407 hand-held computer, hand-held microcomputer 592 | n03485794 handkerchief, hankie, hanky, hankey 593 | n03492542 hard disc, hard disk, fixed disk 594 | n03494278 harmonica, mouth organ, harp, mouth harp 595 | n03495258 harp 596 | n03496892 harvester, reaper 597 | n03498962 hatchet 598 | n03527444 holster 599 | n03529860 home theater, home theatre 600 | n03530642 honeycomb 601 | n03532672 hook, claw 602 | n03534580 hoopskirt, crinoline 603 | n03535780 horizontal bar, high bar 604 | n03538406 horse cart, horse-cart 605 | n03544143 hourglass 606 | n03584254 iPod 607 | n03584829 iron, smoothing iron 608 | n03590841 jack-o'-lantern 609 | n03594734 jean, blue jean, denim 610 | n03594945 jeep, landrover 611 | n03595614 jersey, T-shirt, tee shirt 612 | n03598930 jigsaw puzzle 613 | n03599486 jinrikisha, ricksha, rickshaw 614 | n03602883 joystick 615 | n03617480 kimono 616 | n03623198 knee pad 617 | n03627232 knot 618 | n03630383 lab coat, laboratory coat 619 | n03633091 ladle 620 | n03637318 lampshade, lamp shade 621 | n03642806 laptop, laptop computer 622 | n03649909 lawn mower, mower 623 | n03657121 lens cap, lens cover 624 | n03658185 letter opener, paper knife, paperknife 625 | n03661043 library 626 | n03662601 lifeboat 627 | n03666591 lighter, light, igniter, ignitor 628 | n03670208 limousine, limo 629 | n03673027 liner, ocean liner 630 | n03676483 lipstick, lip rouge 631 | n03680355 Loafer 632 | n03690938 lotion 633 | n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system 634 | n03692522 loupe, jeweler's loupe 635 | n03697007 lumbermill, sawmill 636 | n03706229 magnetic compass 637 | n03709823 mailbag, postbag 638 | n03710193 mailbox, letter box 639 | n03710637 maillot 640 | n03710721 maillot, tank suit 641 | n03717622 manhole cover 642 | n03720891 maraca 643 | n03721384 marimba, xylophone 644 | n03724870 mask 645 | n03729826 matchstick 646 | n03733131 maypole 647 | n03733281 maze, labyrinth 648 | n03733805 measuring cup 649 | n03742115 medicine chest, medicine cabinet 650 | n03743016 megalith, megalithic structure 651 | n03759954 microphone, mike 652 | n03761084 microwave, microwave oven 653 | n03763968 military uniform 654 | n03764736 milk can 655 | n03769881 minibus 656 | n03770439 miniskirt, mini 657 | n03770679 minivan 658 | n03773504 missile 659 | n03775071 mitten 660 | n03775546 mixing bowl 661 | n03776460 mobile home, manufactured home 662 | n03777568 Model T 663 | n03777754 modem 664 | n03781244 monastery 665 | n03782006 monitor 666 | n03785016 moped 667 | n03786901 mortar 668 | n03787032 mortarboard 669 | n03788195 mosque 670 | n03788365 mosquito net 671 | n03791053 motor scooter, scooter 672 | n03792782 mountain bike, all-terrain bike, off-roader 673 | n03792972 mountain tent 674 | n03793489 mouse, computer mouse 675 | n03794056 mousetrap 676 | n03796401 moving van 677 | n03803284 muzzle 678 | n03804744 nail 679 | n03814639 neck brace 680 | n03814906 necklace 681 | n03825788 nipple 682 | n03832673 notebook, notebook computer 683 | n03837869 obelisk 684 | n03838899 oboe, hautboy, hautbois 685 | n03840681 ocarina, sweet potato 686 | n03841143 odometer, hodometer, mileometer, milometer 687 | n03843555 oil filter 688 | n03854065 organ, pipe organ 689 | n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO 690 | n03866082 overskirt 691 | n03868242 oxcart 692 | n03868863 oxygen mask 693 | n03871628 packet 694 | n03873416 paddle, boat paddle 695 | n03874293 paddlewheel, paddle wheel 696 | n03874599 padlock 697 | n03876231 paintbrush 698 | n03877472 pajama, pyjama, pj's, jammies 699 | n03877845 palace 700 | n03884397 panpipe, pandean pipe, syrinx 701 | n03887697 paper towel 702 | n03888257 parachute, chute 703 | n03888605 parallel bars, bars 704 | n03891251 park bench 705 | n03891332 parking meter 706 | n03895866 passenger car, coach, carriage 707 | n03899768 patio, terrace 708 | n03902125 pay-phone, pay-station 709 | n03903868 pedestal, plinth, footstall 710 | n03908618 pencil box, pencil case 711 | n03908714 pencil sharpener 712 | n03916031 perfume, essence 713 | n03920288 Petri dish 714 | n03924679 photocopier 715 | n03929660 pick, plectrum, plectron 716 | n03929855 pickelhaube 717 | n03930313 picket fence, paling 718 | n03930630 pickup, pickup truck 719 | n03933933 pier 720 | n03935335 piggy bank, penny bank 721 | n03937543 pill bottle 722 | n03938244 pillow 723 | n03942813 ping-pong ball 724 | n03944341 pinwheel 725 | n03947888 pirate, pirate ship 726 | n03950228 pitcher, ewer 727 | n03954731 plane, carpenter's plane, woodworking plane 728 | n03956157 planetarium 729 | n03958227 plastic bag 730 | n03961711 plate rack 731 | n03967562 plow, plough 732 | n03970156 plunger, plumber's helper 733 | n03976467 Polaroid camera, Polaroid Land camera 734 | n03976657 pole 735 | n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria 736 | n03980874 poncho 737 | n03982430 pool table, billiard table, snooker table 738 | n03983396 pop bottle, soda bottle 739 | n03991062 pot, flowerpot 740 | n03992509 potter's wheel 741 | n03995372 power drill 742 | n03998194 prayer rug, prayer mat 743 | n04004767 printer 744 | n04005630 prison, prison house 745 | n04008634 projectile, missile 746 | n04009552 projector 747 | n04019541 puck, hockey puck 748 | n04023962 punching bag, punch bag, punching ball, punchball 749 | n04026417 purse 750 | n04033901 quill, quill pen 751 | n04033995 quilt, comforter, comfort, puff 752 | n04037443 racer, race car, racing car 753 | n04039381 racket, racquet 754 | n04040759 radiator 755 | n04041544 radio, wireless 756 | n04044716 radio telescope, radio reflector 757 | n04049303 rain barrel 758 | n04065272 recreational vehicle, RV, R.V. 759 | n04067472 reel 760 | n04069434 reflex camera 761 | n04070727 refrigerator, icebox 762 | n04074963 remote control, remote 763 | n04081281 restaurant, eating house, eating place, eatery 764 | n04086273 revolver, six-gun, six-shooter 765 | n04090263 rifle 766 | n04099969 rocking chair, rocker 767 | n04111531 rotisserie 768 | n04116512 rubber eraser, rubber, pencil eraser 769 | n04118538 rugby ball 770 | n04118776 rule, ruler 771 | n04120489 running shoe 772 | 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/optFlowFarneback/optFlowFarneback/train_val.prototxt: -------------------------------------------------------------------------------- 1 | name: "AlexNet" 2 | layer { 3 | name: "data" 4 | type: "Data" 5 | top: "data" 6 | top: "label" 7 | include { 8 | phase: TRAIN 9 | } 10 | transform_param { 11 | mirror: true 12 | crop_size: 227 13 | mean_file: "data/ilsvrc12/imagenet_mean.binaryproto" 14 | } 15 | data_param { 16 | source: "examples/imagenet/ilsvrc12_train_lmdb" 17 | batch_size: 256 18 | backend: LMDB 19 | } 20 | } 21 | layer { 22 | name: "data" 23 | type: "Data" 24 | top: "data" 25 | top: "label" 26 | include { 27 | phase: TEST 28 | } 29 | transform_param { 30 | mirror: false 31 | crop_size: 227 32 | mean_file: "data/ilsvrc12/imagenet_mean.binaryproto" 33 | } 34 | data_param { 35 | source: "examples/imagenet/ilsvrc12_val_lmdb" 36 | batch_size: 50 37 | backend: LMDB 38 | } 39 | } 40 | layer { 41 | name: "conv1" 42 | type: "Convolution" 43 | bottom: "data" 44 | top: "conv1" 45 | param { 46 | lr_mult: 1 47 | decay_mult: 1 48 | } 49 | param { 50 | lr_mult: 2 51 | decay_mult: 0 52 | } 53 | convolution_param { 54 | num_output: 96 55 | kernel_size: 11 56 | stride: 4 57 | weight_filler { 58 | type: "gaussian" 59 | std: 0.01 60 | } 61 | bias_filler { 62 | type: "constant" 63 | value: 0 64 | } 65 | } 66 | } 67 | layer { 68 | name: "relu1" 69 | type: "ReLU" 70 | bottom: "conv1" 71 | top: "conv1" 72 | } 73 | layer { 74 | name: "norm1" 75 | type: "LRN" 76 | bottom: "conv1" 77 | top: "norm1" 78 | lrn_param { 79 | local_size: 5 80 | alpha: 0.0001 81 | beta: 0.75 82 | } 83 | } 84 | layer { 85 | name: "pool1" 86 | type: "Pooling" 87 | bottom: "norm1" 88 | top: "pool1" 89 | pooling_param { 90 | pool: MAX 91 | kernel_size: 3 92 | stride: 2 93 | } 94 | } 95 | layer { 96 | name: "conv2" 97 | type: "Convolution" 98 | bottom: "pool1" 99 | top: "conv2" 100 | param { 101 | lr_mult: 1 102 | decay_mult: 1 103 | } 104 | param { 105 | lr_mult: 2 106 | decay_mult: 0 107 | } 108 | convolution_param { 109 | num_output: 256 110 | pad: 2 111 | kernel_size: 5 112 | group: 2 113 | weight_filler { 114 | type: "gaussian" 115 | std: 0.01 116 | } 117 | bias_filler { 118 | type: "constant" 119 | value: 0.1 120 | } 121 | } 122 | } 123 | layer { 124 | name: "relu2" 125 | type: "ReLU" 126 | bottom: "conv2" 127 | top: "conv2" 128 | } 129 | layer { 130 | name: "norm2" 131 | type: "LRN" 132 | bottom: "conv2" 133 | top: "norm2" 134 | lrn_param { 135 | local_size: 5 136 | alpha: 0.0001 137 | beta: 0.75 138 | } 139 | } 140 | layer { 141 | name: "pool2" 142 | type: "Pooling" 143 | bottom: "norm2" 144 | top: "pool2" 145 | pooling_param { 146 | pool: MAX 147 | kernel_size: 3 148 | stride: 2 149 | } 150 | } 151 | layer { 152 | name: "conv3" 153 | type: "Convolution" 154 | bottom: "pool2" 155 | top: "conv3" 156 | param { 157 | lr_mult: 1 158 | decay_mult: 1 159 | } 160 | param { 161 | lr_mult: 2 162 | decay_mult: 0 163 | } 164 | convolution_param { 165 | num_output: 384 166 | pad: 1 167 | kernel_size: 3 168 | weight_filler { 169 | type: "gaussian" 170 | std: 0.01 171 | } 172 | bias_filler { 173 | type: "constant" 174 | value: 0 175 | } 176 | } 177 | } 178 | layer { 179 | name: "relu3" 180 | type: "ReLU" 181 | bottom: "conv3" 182 | top: "conv3" 183 | } 184 | layer { 185 | name: "conv4" 186 | type: "Convolution" 187 | bottom: "conv3" 188 | top: "conv4" 189 | param { 190 | lr_mult: 1 191 | decay_mult: 1 192 | } 193 | param { 194 | lr_mult: 2 195 | decay_mult: 0 196 | } 197 | convolution_param { 198 | num_output: 384 199 | pad: 1 200 | kernel_size: 3 201 | group: 2 202 | weight_filler { 203 | type: "gaussian" 204 | std: 0.01 205 | } 206 | bias_filler { 207 | type: "constant" 208 | value: 0.1 209 | } 210 | } 211 | } 212 | layer { 213 | name: "relu4" 214 | type: "ReLU" 215 | bottom: "conv4" 216 | top: "conv4" 217 | } 218 | layer { 219 | name: "conv5" 220 | type: "Convolution" 221 | bottom: "conv4" 222 | top: "conv5" 223 | param { 224 | lr_mult: 1 225 | decay_mult: 1 226 | } 227 | param { 228 | lr_mult: 2 229 | decay_mult: 0 230 | } 231 | convolution_param { 232 | num_output: 256 233 | pad: 1 234 | kernel_size: 3 235 | group: 2 236 | weight_filler { 237 | type: "gaussian" 238 | std: 0.01 239 | } 240 | bias_filler { 241 | type: "constant" 242 | value: 0.1 243 | } 244 | } 245 | } 246 | layer { 247 | name: "relu5" 248 | type: "ReLU" 249 | bottom: "conv5" 250 | top: "conv5" 251 | } 252 | layer { 253 | name: "pool5" 254 | type: "Pooling" 255 | bottom: "conv5" 256 | top: "pool5" 257 | pooling_param { 258 | pool: MAX 259 | kernel_size: 3 260 | stride: 2 261 | } 262 | } 263 | layer { 264 | name: "fc6" 265 | type: "InnerProduct" 266 | bottom: "pool5" 267 | top: "fc6" 268 | param { 269 | lr_mult: 1 270 | decay_mult: 1 271 | } 272 | param { 273 | lr_mult: 2 274 | decay_mult: 0 275 | } 276 | inner_product_param { 277 | num_output: 4096 278 | weight_filler { 279 | type: "gaussian" 280 | std: 0.005 281 | } 282 | bias_filler { 283 | type: "constant" 284 | value: 0.1 285 | } 286 | } 287 | } 288 | layer { 289 | name: "relu6" 290 | type: "ReLU" 291 | bottom: "fc6" 292 | top: "fc6" 293 | } 294 | layer { 295 | name: "drop6" 296 | type: "Dropout" 297 | bottom: "fc6" 298 | top: "fc6" 299 | dropout_param { 300 | dropout_ratio: 0.5 301 | } 302 | } 303 | layer { 304 | name: "fc7" 305 | type: "InnerProduct" 306 | bottom: "fc6" 307 | top: "fc7" 308 | param { 309 | lr_mult: 1 310 | decay_mult: 1 311 | } 312 | param { 313 | lr_mult: 2 314 | decay_mult: 0 315 | } 316 | inner_product_param { 317 | num_output: 4096 318 | weight_filler { 319 | type: "gaussian" 320 | std: 0.005 321 | } 322 | bias_filler { 323 | type: "constant" 324 | value: 0.1 325 | } 326 | } 327 | } 328 | layer { 329 | name: "relu7" 330 | type: "ReLU" 331 | bottom: "fc7" 332 | top: "fc7" 333 | } 334 | layer { 335 | name: "drop7" 336 | type: "Dropout" 337 | bottom: "fc7" 338 | top: "fc7" 339 | dropout_param { 340 | dropout_ratio: 0.5 341 | } 342 | } 343 | layer { 344 | name: "fc8" 345 | type: "InnerProduct" 346 | bottom: "fc7" 347 | top: "fc8" 348 | param { 349 | lr_mult: 1 350 | decay_mult: 1 351 | } 352 | param { 353 | lr_mult: 2 354 | decay_mult: 0 355 | } 356 | inner_product_param { 357 | num_output: 1000 358 | weight_filler { 359 | type: "gaussian" 360 | std: 0.01 361 | } 362 | bias_filler { 363 | type: "constant" 364 | value: 0 365 | } 366 | } 367 | } 368 | layer { 369 | name: "accuracy" 370 | type: "Accuracy" 371 | bottom: "fc8" 372 | bottom: "label" 373 | top: "accuracy" 374 | include { 375 | phase: TEST 376 | } 377 | } 378 | layer { 379 | name: "loss" 380 | type: "SoftmaxWithLoss" 381 | bottom: "fc8" 382 | bottom: "label" 383 | top: "loss" 384 | } 385 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/x64/Debug/Source.obj: -------------------------------------------------------------------------------- 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/optFlowFarneback/optFlowFarneback/x64/Debug/optFlowFarneback.tlog/optFlowFarneback.lastbuildstate: -------------------------------------------------------------------------------- 1 | #TargetFrameworkVersion=v4.0:PlatformToolSet=v120:EnableManagedIncrementalBuild=false:VCToolArchitecture=Native32Bit 2 | Debug|x64|C:\Users\UTRGVCS\Documents\Visual Studio 2013\Projects\optFlowFarneback\| 3 | -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/x64/Debug/vc120.idb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bassamarshad/Dense_OpticalFlow_and_CNN_based_Motion_Segmentation_and_Object_Recognition/4c81240914db464b4f9edb2c4fcd5b7b67b82e95/optFlowFarneback/optFlowFarneback/x64/Debug/vc120.idb -------------------------------------------------------------------------------- /optFlowFarneback/optFlowFarneback/x64/Debug/vc120.pdb: 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8:33:59 PM. 2 | 1>Project "E:\OpticalFlowTrackers-FinalProjectBrownsvilleTeam\optFlowFarneback\optFlowFarneback\optFlowFarneback.vcxproj" on node 2 (Build target(s)). 3 | 1>ClCompile: 4 | C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64\CL.exe /c /I"E:\opencv-3.1.0\opencv\install\x64\vc14\..\..\include" /Zi /nologo /W3 /WX- /sdl /O2 /Oi /GL /D NDEBUG /D _CONSOLE /D _UNICODE /D UNICODE /Gm- /EHsc /MD /GS /Gy /fp:precise /Zc:wchar_t /Zc:forScope /Zc:inline /Fo"x64\Release\\" /Fd"x64\Release\vc140.pdb" /Gd /TP /errorReport:prompt Source.cpp 5 | Source.cpp 6 | 1>e:\opticalflowtrackers-finalprojectbrownsvilleteam\optflowfarneback\optflowfarneback\DNN.h(133): warning C4244: '=': conversion from 'double' to 'float', possible loss of data 7 | 1>Source.cpp(58): warning C4305: 'initializing': truncation from 'double' to 'float' 8 | Link: 9 | C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64\link.exe /ERRORREPORT:PROMPT /OUT:"E:\OpticalFlowTrackers-FinalProjectBrownsvilleTeam\optFlowFarneback\x64\Release\optFlowFarneback.exe" /INCREMENTAL:NO /NOLOGO /LIBPATH:"E:\opencv-3.1.0\opencv\install\x64\vc14\lib" opencv_core310.lib opencv_ts310.lib opencv_video310.lib opencv_videoio310.lib opencv_tracking310.lib opencv_highgui310.lib opencv_imgproc310.lib opencv_imgcodecs310.lib opencv_features2d310.lib opencv_calib3d310.lib opencv_optflow310.lib opencv_dnn310.lib /MANIFEST /MANIFESTUAC:"level='asInvoker' uiAccess='false'" /manifest:embed /Debug /PDB:"E:\OpticalFlowTrackers-FinalProjectBrownsvilleTeam\optFlowFarneback\x64\Release\optFlowFarneback.pdb" /SUBSYSTEM:CONSOLE /OPT:REF /OPT:ICF /LTCG:incremental /TLBID:1 /DYNAMICBASE /NXCOMPAT /IMPLIB:"E:\OpticalFlowTrackers-FinalProjectBrownsvilleTeam\optFlowFarneback\x64\Release\optFlowFarneback.lib" /MACHINE:X64 x64\Release\Source.obj 10 | Generating code 11 | 0 of 571 functions ( 0.0%) were compiled, the rest were copied from previous compilation. 12 | 0 functions 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