文件列表:

.circleci (0, 2018-08-06)
.circleci\config.yml (500, 2018-08-06)
3rdparty (0, 2018-08-06)
3rdparty\dll (0, 2018-08-06)
3rdparty\dll\x64 (0, 2018-08-06)
3rdparty\dll\x64\pthreadGC2.dll (185976, 2018-08-06)
3rdparty\dll\x64\pthreadVC2.dll (82944, 2018-08-06)
3rdparty\dll\x86 (0, 2018-08-06)
3rdparty\dll\x86\pthreadGC2.dll (119888, 2018-08-06)
3rdparty\dll\x86\pthreadGCE2.dll (121953, 2018-08-06)
3rdparty\dll\x86\pthreadVC2.dll (55808, 2018-08-06)
3rdparty\dll\x86\pthreadVCE2.dll (61952, 2018-08-06)
3rdparty\dll\x86\pthreadVSE2.dll (57344, 2018-08-06)
3rdparty\include (0, 2018-08-06)
3rdparty\include\pthread.h (42499, 2018-08-06)
3rdparty\include\sched.h (4995, 2018-08-06)
3rdparty\include\semaphore.h (4563, 2018-08-06)
3rdparty\lib (0, 2018-08-06)
3rdparty\lib\x64 (0, 2018-08-06)
3rdparty\lib\x64\libpthreadGC2.a (93692, 2018-08-06)
3rdparty\lib\x64\pthreadVC2.lib (29738, 2018-08-06)
3rdparty\lib\x86 (0, 2018-08-06)
3rdparty\lib\x86\libpthreadGC2.a (93480, 2018-08-06)
3rdparty\lib\x86\libpthreadGCE2.a (93486, 2018-08-06)
3rdparty\lib\x86\pthreadVC2.lib (30334, 2018-08-06)
3rdparty\lib\x86\pthreadVCE2.lib (30460, 2018-08-06)
3rdparty\lib\x86\pthreadVSE2.lib (30460, 2018-08-06)
LICENSE (515, 2018-08-06)
Makefile (4279, 2018-08-06)
build (0, 2018-08-06)
build\darknet (0, 2018-08-06)
build\darknet\YoloWrapper.cs (3039, 2018-08-06)
build\darknet\darknet.sln (1283, 2018-08-06)
build\darknet\darknet.vcxproj (16236, 2018-08-06)
build\darknet\darknet_no_gpu.sln (1281, 2018-08-06)
build\darknet\darknet_no_gpu.vcxproj (15508, 2018-08-06)
build\darknet\x64 (0, 2018-08-06)
... ...

# Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) [](https://circleci.com/gh/AlexeyAB/darknet) 1. [How to use](#how-to-use) 2. [How to compile on Linux](#how-to-compile-on-linux) 3. [How to compile on Windows](#how-to-compile-on-windows) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) 5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) 6. [When should I stop training](#when-should-i-stop-training) 7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) 8. [How to improve object detection](#how-to-improve-object-detection) 9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 10. [Using Yolo9000](#using-yolo9000) 11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |  |    mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg # "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors This repository is forked from Linux-version: https://github.com/pjreddie/darknet More details: http://pjreddie.com/darknet/yolo/ This repository supports: * both Windows and Linux * both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported) * both cuDNN v5-v7 * CUDA >= 7.5 * also create SO-library on Linux and DLL-library on Windows ##### Requires: * **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) * **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads * **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights * `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights * `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` ##### Examples of results: [](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg ### How to use: ##### Example of usage in cmd-files from `build\darknet\x***\`: * `darknet_yolo_v3.cmd` - initialization with 236 MB **Yolo v3** COCO-model yolov3.weights & yolov3.cfg and show detection on the image: dog.jpg * `darknet_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file * `darknet_demo_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4 * `darknet_demo_store.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: res.avi * `darknet_net_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) * `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0 * `darknet_coco_9000.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg * `darknet_coco_9000_demo.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4, and store result to: res.avi ##### How to use on the command line: On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` * **Yolo v3** COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -thresh 0.25 dog.jpg -ext_output` * 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0` * 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` * 194 MB VOC-model - **save result to the file res.avi**: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi` * Alternative method 194 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` * 43 MB VOC-model for video: `darknet.exe detector demo data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights test.mp4 -i 0` * **Yolo v3** 236 MB COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` * 194 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` * 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` ##### For using network video-camera mjpeg-stream with any Android smartphone: 1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam * Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 * IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam 2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: * 194 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` * 194 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` ### How to compile on Linux: Just do `make` in the darknet directory. Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) * `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x * `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams * `DEBUG=1` to bould debug version of Yolo * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4` ### How to compile on Windows: 1. If you have **MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x***\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x***** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_***.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_***.dll`) in `C:\opencv_3.0\opencv\build\x***\vc14\bin` and put it near with `darknet.exe` 1.2 Check that there are `bin` and `include` folders in the `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1` if aren't, then copy them to this folder from the path where is CUDA installed 1.3. To install CUDNN (speedup neural network), do the following: * download and install **cuDNN 7.0 for CUDA 9.1**: https://developer.nvidia.com/cudnn * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg 1.4. If you want to build **without CUDNN** then: open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: `CUDNN;` 2. If you have other version of **CUDA (not 9.1)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x***\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x***** and **Release**, and do the: Build -> Build darknet_no_gpu 4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change pathes after `\darknet.sln` is opened 4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_2.4.13\opencv\build\include` 4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x***\vc14\lib` 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;` **Note:** CUDA must be installed only after that MSVS2015 had been installed. ### How to compile (custom): Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 9.1 and OpenCV 3.0 Then add to your created project: - (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here: `C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` - (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg - add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src` - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: `C:\opencv_3.0\opencv\build\x***\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x***;%(AdditionalLibraryDirectories)` - (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here: `..\..\3rdparty\lib\x***\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)` - (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` - compile to .exe (X*** & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg * `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x*** * `cusolver***_91.dll, curand***_91.dll, cudart***_91.dll, cublas***_91.dll` - 91 for CUDA 9.1 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin * For OpenCV 3.2: `opencv_world320.dll` and `opencv_ffmpeg320_***.dll` from `C:\opencv_3.0\opencv\build\x***\vc14\bin` * For OpenCV 2.4.13: `opencv_core2413.dll`, `opencv_highgui2413.dll` and `opencv_ffmpeg2413_***.dll` from `C:\opencv_2.4.13\opencv\build\x***\vc14\bin` ## How to train (Pascal VOC Data): 1. Download pre-trained weights for the convolutional layers (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x***` 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x***\data\voc` will be created dir `build\darknet\x***\data\voc\VOCdevkit\`: * http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar * http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar * http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar 2.1 Download file `voc_label.py` to dir `build\darknet\x***\data\voc`: http://pjreddie.com/media/files/voc_label.py 3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd***.exe 4. Run command: `python build\darknet\x***\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` 6. Set `batch=***` and `subdivisions=8` in the file `yolov3-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/ee38c6e1513fb089b35be4ffa692afd9b3f65747/cfg/yolov3-voc.cfg#L3-L4) 7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) If required change pathes in the file `build\darknet\x***\data\voc.data` More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. ## How to train with multi-GPU: 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` 2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ ## How to train (to detect your custom objects): (to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e***18cb8b1b4f#how-to-train-pascal-voc-data)) Training Yolo v3: 1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=***`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783 * change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=21`. **(Do not write in the cfg-file: filters=(classes + 5)x3)** (Generally `filters` depends on the `classes`, `coords` and number of `mask`s, i.e. filters=`(classes + coords + 1)*`, where `mask` is indices of anchors. If `mask` is absence, then filters=`(classes + coords + 1)*num`) So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov3.cfg` in such lines in each of **3** [yolo]-layers: ``` [convolutional] filters=21 [region] classes=2 ``` 2. Create file `obj.names` in the directory `build\darknet\x***\data\`, with objects names - each in new line 3. Create file `obj.data` in the directory `build\darknet\x***\data\`, containing (where **classes = number of objects**): ``` classes= 2 train = data/train.txt valid = data/test.txt names = data/obj.names backup = backup/ ``` 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x***\data\obj\` 5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each obj ... ...

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