item { name: "/m/01g317" id: 1 display_name: "person" } item { name: "/m/0199g" id: 2 display_name: "bicycle" } item { name: "/m/0k4j" id: 3 display_name: "car" } item { name: "/m/04_sv" id: 4 display_name: "motorcycle" } item { name: "/m/05czz6l" id: 5 display_name: "airplane" } item { name: "/m/01bjv" id: 6 display_name: "bus" } item { name: "/m/07jdr" id: 7 display_name: "train" } item { name: "/m/07r04" id: 8 display_name: "truck" } item { name: "/m/019jd" id: 9 display_name: "boat" } item { name: "/m/015qff" id: 10 display_name: "traffic light" } item { name: "/m/01pns0" id: 11 display_name: "fire hydrant" } item { name: "/m/02pv19" id: 13 display_name: "stop sign" } item { name: "/m/015qbp" id: 14 display_name: "parking meter" } item { name: "/m/0cvnqh" id: 15 display_name: "bench" } item { name: "/m/015p6" id: 16 display_name: "bird" } item { name: "/m/01yrx" id: 17 display_name: "cat" } item { name: "/m/0bt9lr" id: 18 display_name: "dog" } item { name: "/m/03k3r" id: 19 display_name: "horse" } item { name: "/m/07bgp" id: 20 display_name: "sheep" } item { name: "/m/01xq0k1" id: 21 display_name: "cow" } item { name: "/m/0bwd_0j" id: 22 display_name: "elephant" } item { name: "/m/01dws" id: 23 display_name: "bear" } item { name: "/m/0898b" id: 24 display_name: "zebra" } item { name: "/m/03bk1" id: 25 display_name: "giraffe" } item { name: "/m/01940j" id: 27 display_name: "backpack" } item { name: "/m/0hnnb" id: 28 display_name: "umbrella" } item { name: "/m/080hkjn" id: 31 display_name: "handbag" } item { name: "/m/01rkbr" id: 32 display_name: "tie" } item { name: "/m/01s55n" id: 33 display_name: "suitcase" } item { name: "/m/02wmf" id: 34 display_name: "frisbee" } item { name: "/m/071p9" id: 35 display_name: "skis" } item { name: "/m/06__v" id: 36 display_name: "snowboard" } item { name: "/m/018xm" id: 37 display_name: "sports ball" } item { name: "/m/02zt3" id: 38 display_name: "kite" } item { name: "/m/03g8mr" id: 39 display_name: "baseball bat" } item { name: "/m/03grzl" id: 40 display_name: "baseball glove" } item { name: "/m/06_fw" id: 41 display_name: "skateboard" } item { name: "/m/019w40" id: 42 display_name: "surfboard" } item { name: "/m/0dv9c" id: 43 display_name: "tennis racket" } item { name: "/m/04dr76w" id: 44 display_name: "bottle" } item { name: "/m/09tvcd" id: 46 display_name: "wine glass" } item { name: "/m/08gqpm" id: 47 display_name: "cup" } item { name: "/m/0dt3t" id: 48 display_name: "fork" } item { name: "/m/04ctx" id: 49 display_name: "knife" } item { name: "/m/0cmx8" id: 50 display_name: "spoon" } item { name: "/m/04kkgm" id: 51 display_name: "bowl" } item { name: "/m/09qck" id: 52 display_name: "banana" } item { name: "/m/014j1m" id: 53 display_name: "apple" } item { name: "/m/0l515" id: 54 display_name: "sandwich" } item { name: "/m/0cyhj_" id: 55 display_name: "orange" } item { name: "/m/0hkxq" id: 56 display_name: "broccoli" } item { name: "/m/0fj52s" id: 57 display_name: "carrot" } item { name: "/m/01b9xk" id: 58 display_name: "hot dog" } item { name: "/m/0663v" id: 59 display_name: "pizza" } item { name: "/m/0jy4k" id: 60 display_name: "donut" } item { name: "/m/0fszt" id: 61 display_name: "cake" } item { name: "/m/01mzpv" id: 62 display_name: "chair" } item { name: "/m/02crq1" id: 63 display_name: "couch" } item { name: "/m/03fp41" id: 64 display_name: "potted plant" } item { name: "/m/03ssj5" id: 65 display_name: "bed" } item { name: "/m/04bcr3" id: 67 display_name: "dining table" } item { name: "/m/09g1w" id: 70 display_name: "toilet" } item { name: "/m/07c52" id: 72 display_name: "tv" } item { name: "/m/01c648" id: 73 display_name: "laptop" } item { name: "/m/020lf" id: 74 display_name: "mouse" } item { name: "/m/0qjjc" id: 75 display_name: "remote" } item { name: "/m/01m2v" id: 76 display_name: "keyboard" } item { name: "/m/050k8" id: 77 display_name: "cell phone" } item { name: "/m/0fx9l" id: 78 display_name: "microwave" } item { name: "/m/029bxz" id: 79 display_name: "oven" } item { name: "/m/01k6s3" id: 80 display_name: "toaster" } item { name: "/m/0130jx" id: 81 display_name: "sink" } item { name: "/m/040b_t" id: 82 display_name: "refrigerator" } item { name: "/m/0bt_c3" id: 84 display_name: "book" } item { name: "/m/01x3z" id: 85 display_name: "clock" } item { name: "/m/02s195" id: 86 display_name: "vase" } item { name: "/m/01lsmm" id: 87 display_name: "scissors" } item { name: "/m/0kmg4" id: 88 display_name: "teddy bear" } item { name: "/m/03wvsk" id: 89 display_name: "hair drier" } item { name: "/m/012xff" id: 90 display_name: "toothbrush" }
10/10/2018
pbtxt file for coco v2 data (90 categories contents)
Labels:
coco v2,
Deep learning,
google object detection api,
pbtxt,
Total
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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm...
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As you can see in the following video, I created a class that stitching n cameras in real time. https://www.youtube.com/user/feelmare/sear...
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Video stabilization example source code. The principle is like that... Firstly, to obtain 2 adjacent images extract good feature to t...
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go to project settings find python in plugins category and put your "site-packages" path on addtional paths -> index[0] input ...
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My Environment : MS VS 2008 & MFC(Dialog Based) Joy Stick : Logitech Extreme 3D pro (XBox Type) Cteated Date : 2012. 03 [source code]...
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Created Date : 2011.10 Language : C/C++ Tool : Microsoft Visual C++ 2008 Library & Utilized : OpenCV 2.3 Reference : SIFT referenc...
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In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv <-> rgb converting example code. refer to this page -> ht...
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This is GPU version of this page -> http://feelmare.blogspot.kr/2013/12/finding-largest-subset-images-that-is.html Please refer detail ...
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numberList = [ 1 , 2 , 3 , 4 , 5 ] strList = [ 'one' , 'two' , 'three' , 'five' ] # No iterables are passe...
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Created Date : 2007.8 Language : Matlab / C++(MFC) Tool : Matlab / Visual C++ 6.0 Library & Utilized : - / OpenGL Reference : ...
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