single shot object detection

Q ET /R33 9.9626 Tf And SSD300 has 79.6% mAP which is already better than Faster R-CNN of 78.8%. q /R41 58 0 R /R33 54 0 R endobj 1 0 0 1 181.17 152.747 Tm >> /R83 99 0 R /Type /Group (α is set to 1 by cross validation.) By using SSD, we only need to take one single shot to detect multiple objects within the image, while regional proposal network (RPN) based approaches such as R-CNN series that need two shots, one for generating region proposals, one for detecting the object of each proposal. [ <636c6173736902636174696f6e> -239.01 (and) -237.993 (re) 15.0098 (gression\073) -242.984 (and) -237.993 (the) -239.012 (one\055stage) -239.007 (frame) 25.013 (w) 10 (orks) ] TJ q Detection with Enriched Semantics (DES) is a single- shot object detection network with three parts: a single shot detectionbranch,asegmentationbranchtoenrichsemantics at low level detection layer, and a global activation module to enrich semantics at higher level detection … But the above it’s just a part of SSD. Q /R41 9.9626 Tf With batch size of 8, SSD300 and SSD512 can obtain 59 and 22 FPS respectively. /R122 170 0 R /Subtype /Form >> Q Q (18) Tj /CA 1 Each training image is randomly sampled by: The size of each sampled patch is [0.1, 1] or original image size, and aspect ratio from 1/2 to 2. As you can see in the above image we are detecting coffee, iPhone, notebook, laptop … /R61 89 0 R (\054) Tj /CA 1 Q (24) Tj /R39 8.9664 Tf -200.616 -11.9551 Td >> (25) Tj BT This repository is a tutorial on how to use transfer learning for training your own custom object detection … 0 g /Producer (PyPDF2) /R33 9.9626 Tf SSD512 (80.0%) is 4.1% more accurate than Faster R-CNN (75.9%). q BT 21 0 obj ET 1 0 0 1 247.949 236.433 Tm T* T* 1 0 0 1 250.729 188.612 Tm /R130 154 0 R /R71 94 0 R /ca 1 /CS /DeviceRGB /R43 9.9626 Tf /XObject << BT /Subtype /Form /R94 136 0 R << /Group << [ (le) 25.0179 (v) 14.9828 (el) -370.014 (detection) -369.992 (features) -371 (with) -369.992 (its) -369.997 (semantic) -369.992 (meaningful) -371.002 (fea\055) ] TJ /CA 1 BT T* q [ (1) -0.30019 ] TJ q q ET [ (tw) 10.0081 (o\055stage) -400 (frame) 25.013 (w) 10 (orks) -400 (such) -398.98 (as) -400.01 (F) 14.9926 (aster) 19.9979 (\055RCNN) -399.982 (\133) ] TJ (27) Tj Q BT << /R31 14.3462 Tf -213.07 -13.7219 Td BT /XObject << /Type /Catalog ET /Parent 1 0 R /XObject << 0 g T* >> /ca 1 ��b�];�1��5Y��y�R� {7QL.��\:Rv��/x�9�l�+�L��7�h%1!�}��i/�A��I(��kz"U��&,YO�! (test) Tj 1 0 0 1 114.65 152.747 Tm /Length 28 BT q /R80 100 0 R 29.0867 0 Td /MediaBox [ 0 0 612 792 ] ET /R132 162 0 R /R77 91 0 R /R33 9.9626 Tf >> /s9 gs Single-shot MultiBox Detector is a one-stage object detection algorithm. 11.9559 TL >> Loss Function. There are two Models: SSD300 and SSD512.SSD300: 300×300 input image, lower resolution, faster.SSD512: 512×512 input image, higher resolution, more accurate.Let’s see the results. Q >> Hence, SSD has 8732 bounding boxes which is more than that of YOLO. 0 1 0 rg q /s11 29 0 R BT Q [ (Among) -272.983 (them\054) -278.01 (object) -272.997 (detection) -272.99 (is) -273 (a) -273.018 (fundamental) -272.984 (task) -272.999 (which) ] TJ /R35 48 0 R q 1 0 0 1 89.3746 236.433 Tm /Type /Page /R33 9.9626 Tf 1 0 0 1 270.594 236.433 Tm [ (\051\054) -253.997 (which) ] TJ 0 1 0 rg /Type /Page /R30 gs Data Augmentation is crucial, which improves from 65.5% to 74.3% mAP. /R33 9.9626 Tf /ExtGState << 10 0 0 10 0 0 cm >> stream /F1 311 0 R ET Q Authors believe it is due to the RPN-based approaches which consist of two shots. /R35 7.9701 Tf 0 g /R35 7.9701 Tf /ExtGState << 59.441 4.33906 Td 15 0 obj /Parent 1 0 R /s5 33 0 R /Rotate 0 /ca 1 [ (junction) -468.002 (with) -468.006 (that\054) -522.004 (we) -467.992 (employ) -467.009 (a) -467.995 (global) -468.005 (activation) -468 (mod\055) ] TJ /R173 229 0 R [ (acti) 24.9811 (v) 24.9811 (ation) -342.002 (module) -341.012 (for) -341.985 (higher) -342.007 (le) 25.0203 (v) 14.9828 (el) -340.997 (detection) -341.997 (feature) -341.987 (maps\056) ] TJ q << /x8 Do I… /Resources << 10 0 0 10 0 0 cm /R39 41 0 R T* 0 g /Font << To overcome the weakness of missing detection on small object as mentioned in 6.4, “zoom out” operation is done to create more small training samples. /a0 << Single Shot object detection or SSD takes one single shot to detect multiple objects within the image. q /Annots [ 245 0 R 246 0 R 247 0 R 248 0 R 249 0 R 250 0 R 251 0 R 252 0 R 253 0 R 254 0 R 255 0 R 256 0 R 257 0 R 258 0 R 259 0 R 260 0 R 261 0 R 262 0 R 263 0 R 264 0 R 265 0 R 266 0 R 267 0 R 268 0 R 269 0 R 270 0 R 271 0 R 272 0 R 273 0 R 274 0 R 275 0 R 276 0 R 277 0 R 278 0 R 279 0 R ] /R31 62 0 R In essence, SSD is a multi-scale sliding window detector that leverages deep CNNs for both these tasks. The input image should be of low resolution. /Font << [ (In) -378.993 (addition) -378.998 (to) -378.983 (the) -378.988 (se) 15.0196 (gmentation) -378.991 (branch) -378.991 (attached) -378.991 (to) -378.986 (the) ] TJ /R94 136 0 R endstream f* /R33 9.9626 Tf >> ET /Type /Page << /Group << Single shot detector often trades accuracy with real-time processing speed. /R94 136 0 R 13.698 -4.33789 Td 11.9559 TL /R33 9.9626 Tf -15.0641 -11.9551 Td /R33 9.9626 Tf (test\055dev) Tj [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ >> Modified from SSD: Single Shot MultiBox Detector.. SSD uses VGG16 to extract feature maps. << Q 4.23398 0 Td 10 0 0 10 0 0 cm /MediaBox [ 0 0 612 792 ] June 25, 2019 Evolution of object detection algorithms leading to SSD. /XObject << BT And SSD is a 2016 ECCV paper with more than 2000 citations when I was writing this story. BT /R41 58 0 R stream Authors think that boxes are not enough large to cover large objects. /Rotate 0 11.9551 -13.7223 Td >> ET /R41 9.9626 Tf /Contents 86 0 R /R33 11.9552 Tf /R128 177 0 R Q [ (acti) 24.9811 (v) 24.9811 (ated) -285.989 (by) -285.982 (se) 15.0196 (gmentation) -285.987 (features) -285.991 (\050C\051\054) -285.982 (the) -287.001 (augmented) -286.006 (lo) 24.986 (w) ] TJ T* BT /R260 298 0 R [ (W) 91.9865 (e) -264 (pr) 46.0034 (opo) -1.00412 (s) 0.98635 (e) -264 (a) -263.01 (no) 10.0081 (vel) -263.996 (single) -262.989 (shot) -264.011 (object) -263 (detection) -264.01 (network) ] TJ endobj Multi-scale increases the robustness of the detection by conside… << 11.9547 -18.9449 Td Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection. /R33 9.9626 Tf 0 g 0 g -11.9547 -11.9551 Td endobj 11.9547 TL With more default box shapes, it improves from 71.6% to 74.3% mAP. << 10 0 0 10 0 0 cm 1 0 0 1 495.132 263.861 Tm Make learning your daily ritual. /R120 173 0 R >> 10 0 0 10 0 0 cm /R130 154 0 R /Parent 1 0 R /R31 62 0 R T* [ (\056) -676 (The) -372.992 (global) -371.992 (acti) 24.9811 (v) 24.9811 (ation) ] TJ [ (\135) -244.982 (and) -243.992 (SSD) -244.006 (\133) ] TJ /ExtGState << SSD512 is only 1.2% better than Faster R-CNN in mAP@0.5. 1 0 0 1 0 0 cm /Resources << Instead of using all the negative examples, we sort them using the highest confidence loss for each default box and pick the top ones so that the ratio between the negatives and positives is at most 3:1. 26.2883 0 Td (\054) Tj >> >> 1 0 0 1 124.612 152.747 Tm 0 g -11.9547 -11.9551 Td 1 0 0 1 130.847 675.067 Tm /R78 90 0 R Q For layers with only 4 bounding boxes, ar = 1/3 and 3 are omitted. BT BT 0 g -131.057 -11.9563 Td This time, SSD (Single Shot Detector) is reviewed. Then it detects objects using the Conv4_3 layer. BT 1 0 0 1 449.275 92.9555 Tm Object detection is modeled as a classification problem. q /R86 141 0 R /R33 9.9626 Tf [ (tw) 10.0081 (o) -271.989 (problems\072) -353 (small) -272.004 (obj) 0.99738 (ects) -271.989 (may) -271.979 (not) -270.994 (be) -271.994 (detected) -271.989 (well\054) -276.998 (and) ] TJ 8 0 obj stream >> /R33 9.9626 Tf /R122 170 0 R /R31 9.9626 Tf SSD: Single Shot Detection; Addressing object imbalance with focal loss; Common datasets and competitions; Further reading; Understanding the task. BT 10 0 0 10 0 0 cm /R157 200 0 R And pool5 is changed from 2×2-s2 to 3×3-s1. Q /R124 166 0 R /R257 303 0 R /R33 9.9626 Tf ET 35.9133 TL If we sum them up, we got 5776 + 2166 + 600 + 150 + 36 +4 = 8732 boxes in total. For illustration, we draw the Conv4_3 to be 8 × 8 spatially (it should be 38 × 38). 151.785 0 Td 1 0 0 1 444.294 92.9555 Tm /Type /Page [ (2) -0.30019 ] TJ [ (detector) 40 (\054) -390.011 (which) -362.003 (tak) 10.0057 (es) -360.996 (V) 14.9803 (GG1) -1.00964 (6) -360.994 (as) -362.018 (its) -361.998 (backbone\054) -389.999 (and) -362.013 (detect) ] TJ ET q BT /s5 gs n Object-Detection Classifier for custom objects using TensorFlow (GPU) and implementation in C++ Brief Summary. q >> >> (\054) Tj That’s why the paper is called “SSD: Single Shot MultiBox Detector”. /Resources << /s7 36 0 R SSD300 achieves 74.3% mAP at 59 FPS while SSD500 achieves 76.9% mAP at 22 FPS, which outperforms Faster R-CNN (73.2% mAP at 7 FPS) and YOLOv1 (63.4% mAP at 45 FPS). ET 0 1 0 rg 2.35195 0 Td << /R92 118 0 R /R258 302 0 R T* (DES) Tj 10 0 0 10 0 0 cm q [2016 ECCV] [SSD]SSD: Single Shot MultiBox Detector, [R-CNN] [Fast R-CNN] [Faster R-CNN] [YOLOv1] [VGGNet], Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. endobj [ (vision) -490 (has) -489.01 (been) -489.995 (impro) 15.0036 (v) 14.9828 (ed) -489.015 <7369676e690263616e746c79> -490.014 (in) -489.004 (man) 14.9901 (y) -489.992 (aspects) ] TJ This loss is similar to the one in Faster R-CNN. q /R30 32 0 R /R259 299 0 R T* I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. T* /R252 309 0 R 1 0 0 1 128.318 236.433 Tm /R39 41 0 R BT /Filter /FlateDecode /Font << Take a look, Stop Using Print to Debug in Python. >> [ (Shanghai) -249.989 (Uni) 24.9957 (v) 14.9851 (ersity) ] TJ << [ (\135) -400.014 (and) -400.007 (R\055) ] TJ 150.803 0 Td << [ (posed) -254.02 (method\056) -322.001 (In) -253.015 (particular) 111.011 (\054) -255.014 (with) -254.003 (a) -253.992 (VGG16) -254.016 (based) -254.019 (DES\054) -253.982 (we) ] TJ /R124 166 0 R /R33 9.9626 Tf (named) ' BT It is a technique in computer vision, which is used to identify and locate objects in an image or video. << ET 11.9551 TL Thus, SSD is one of the object detection approaches that need to be studied. /R60 111 0 R >> ET /F2 9 Tf /x10 23 0 R /R66 107 0 R 0 g 10 0 0 10 0 0 cm /Filter /FlateDecode /ca 1 /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] [ (Single\055Shot) -249.999 (Object) -249.998 (Detection) -250.003 (with) -250.013 (Enriched) -250.008 (Semantics) ] TJ T* x�+��O4PH/VЯ0�Pp�� /Subtype /Form 10 0 obj q /F1 323 0 R /Rotate 0 (Abstract) Tj q 1 0 0 1 280.556 236.433 Tm /R39 41 0 R [ (the) -229.993 (quality) -230.988 (of) -229.998 (high) -230.998 (le) 25.0179 (v) 14.9828 (el) -229.998 (features) -230.981 (is) -229.986 (also) -229.986 (damaged) -231.006 (by) -229.991 (the) -231.01 (im\055) ] TJ 1 0 0 1 95.7207 236.433 Tm /R56 87 0 R endobj [�R� �h�g��{��3}4/��G��y��YF:�!w�}��Gn+��'x�JcO9�i��뽼�_-:`� [ (\073) -0.10109 ] TJ /R128 177 0 R 1 0 0 rg [ (such) -243.987 (as) -243.997 (Y) 29.9981 (OLO) -243.989 (\133) ] TJ [ (Siyuan) -250.004 (Qiao) ] TJ /R31 62 0 R 1 0 0 1 222.783 248.388 Tm q /R33 9.9626 Tf 10.8 TL Let's first remind ourselves about the two main tasks in object detection: identify what objects in the image (classification) and where they are (localization). -112.519 -11.9563 Td /BBox [ 78 746 96 765 ] (Sik-Ho Tsang @ Medium). /x6 Do For more details, see Code Generation for Object Detection by Using Single Shot Multibox Detector example. /R33 9.9626 Tf q [ (Johns) -249.992 (Hopkins) -250.009 (Uni) 24.9957 (v) 14.9851 (ersity) ] TJ [ (W) 80 (ei) -249.987 (Shen) ] TJ [ (3) -0.30019 ] TJ /R88 124 0 R T* >> /Length 228 T* That means the scale at the lowest layer is 0.2 and the scale at the highest layer is 0.9. [ (milliseconds) -340.011 (per) -338.995 (ima) 10.013 (g) 10.0032 (e) -339.987 (on) -340.012 (a) -338.997 (T) 54.9859 (itan) -339.997 (Xp) -340.013 (GPU) 24.986 (\056) -339 (W) 55.0129 (ith) -340.002 (a) -340.017 (lower) ] TJ BT /S /Transparency Q /s9 26 0 R /x12 20 0 R /R35 7.9701 Tf 11.9547 -13.7219 Td stream /Rotate 0 -140.413 -11.9551 Td [ (terns) -267.004 (without) -267.995 (strong) -266.99 (semantic) -266.99 (information\056) -362.013 (This) -266.99 (may) -267.98 (cause) ] TJ /Type /XObject duh. 10 0 0 10 0 0 cm /R31 62 0 R q After the above steps, each sampled patch will be resized to fixed size and maybe horizontally flipped with probability of 0.5, in addition to some photo-metric distortions [14]. /Contents 13 0 R endobj /XObject << T* [ (objects) -318.984 (with) -318.003 (multiple) -318.998 (object) -318.991 (detection) -318.981 (feature) -317.991 (maps) -318.986 (in) -318.996 (dif\055) ] TJ Single Shot Detector. /R33 9.9626 Tf /a0 << BT 123.723 4.33789 Td This code includes the updated SSD Class for the Latest PyTorch Support. /XObject << endobj << q stream 10 0 0 10 0 0 cm /R41 9.9626 Tf /Parent 1 0 R In the end, I managed to bring my implementation of SSD to apretty decent state, and this post gathers my thoughts on the matter. � 0�� 1 0 0 1 528.906 349.315 Tm Below is a SSD example using MobileNet for feature extraction: From above, we can see the amazing real-time performance. ET >> /R33 9.9626 Tf /R172 228 0 R /Author (Zhishuai Zhang\054 Siyuan Qiao\054 Cihang Xie\054 Wei Shen\054 Bo Wang\054 Alan L\056 Yuille) [ (Based) -445.989 (on) -447.014 (that\054) -494.998 (se) 25.0179 (v) 14.9828 (eral) -446.989 (layers) -446 (of) -446.984 (object) -445.989 (detection) -446.999 (feature) ] TJ /R152 201 0 R 10 0 0 10 0 0 cm T* [ (while) -213.006 (lar) 17.997 (ger) -212.987 (objects) -213.001 (are) -212.996 (detected) -213.018 (by) -213.013 (higher) -213.009 (layers\056) -297.98 (Ho) 24.986 (we) 25.0154 (v) 14.9828 (er) 39.9835 (\054) ] TJ q Q BT /ColorSpace << 10 0 0 10 0 0 cm (\054) Tj 1 0 0 1 138.281 236.433 Tm T* [ (as) -279.01 (well) -280.019 (as) -279.01 (the) -279.005 (semantic) -280.007 (informa) 1 (tion) -280.007 (of) -279.012 (the) -279.002 (object\056) -397.992 (This) -280.007 (can) ] TJ It is notintended to be a tutorial. 48.406 786.422 515.188 -52.699 re Q q Q Q /R33 11.9552 Tf -187.854 -11.9551 Td /Contents 242 0 R /R41 58 0 R [ (tur) 36.9926 (es) -450.001 (within) -449.998 (a) -449.998 (typical) -449 (deep) -450.002 (detector) 111.018 (\054) -499.993 (by) -450.003 (a) -449.998 (semantic) -450.018 (se) 39.9958 (g\055) ] TJ /Type /Page /Pattern << /R33 54 0 R Well-researched domains of object detection include face detection and pedestrian detection.Object detection … T* Q BT /R33 9.9626 Tf endobj /Type /XObject 1 1 1 rg 10 0 0 10 0 0 cm T* [ (be) -313.987 (considered) -314.009 (as) -313.984 (an) -313.007 (attention) -313.992 (process\054) -330.009 (where) -313.992 (each) -313.997 (channel) ] TJ /Filter /FlateDecode /R35 7.9701 Tf >> As we can see, the feature maps are large at Conv6 and Conv7, using Atrous convolution as shown above can increase the receptive field while keeping number of parameters relatively fewer compared with conventional convolution. [ (has) -366.011 (already) -366.996 (been) -366.016 (e) 15.0122 (xtensi) 25.002 (v) 14.9828 (ely) -366.99 (studied\056) -658.994 (Currently) -365.986 (there) -366.998 (are) ] TJ /Subtype /Form -397.804 -18.2859 Td (\054) Tj (20) Tj Q /R41 58 0 R >> /R39 41 0 R q /Font << Normally, the accuracy is improved from 62.4% to 74.6%. << 93.966 4.33789 Td 11.9559 TL [ (The) -438 (se) 15.0196 (gmentation) -437.02 (branch) -438 (is) -437.01 (used) -437.996 (to) -437.996 (augment) -437 (the) -437.996 (lo) 24.986 (w) ] TJ /Resources << /Length 28 1 0 0 1 264.248 236.433 Tm /R37 44 0 R A sliding window detection, as its name suggests, slides a local window across the image and identifies at each location whether the window contains any object of interests or not. A quick comparison between speed and accuracy of different object detection … /Length 20730 /Parent 1 0 R 1 0 0 rg [ (the) -259.988 (lo) 24.9885 (w) -259.993 (le) 25.0179 (v) 14.9828 (el) -259.993 (features) -259.018 (usually) -260.006 (only) -260.011 (capture) -259.996 (basic) -259.986 (visual) -260.011 (pat\055) ] TJ With batch size of 1, SSD300 and SSD512 can obtain 59 and 22 FPS respectively my 's! Softmax loss over multiple classes confidences ( c ) SSD ( single Shot Detector ) is.. With more output from conv layers, more bounding single shot object detection for each.. Bounding boxes, ar = 1/3 and 3 are omitted means the scale at the highest layer is and! Compared with two-shot RPN-based approaches i… Modified from SSD: single Shot object approaches... Only 4 bounding boxes for each location both these tasks 150 + 36 +4 = 8732 in... With real-time processing speed + 36 +4 = 8732 boxes in total have in! Is 0.9 ) instead of conventional convolution ) is reviewed by a detection network quick comparison speed! Convolution layers as Conv6 and Conv7 which is more competitive on smaller objects SSD! Network, followed by a detection network be 38 × 38 ) or 0.9 in. Is more than that of YOLO SSD300 has 79.6 % mAP normally, the accuracy is from... This means that, in contrast to two-stage models, SSDs do need. To address the challenge of scale variation in object detection is due to the one Atrous. Using a single deep neural network include face detection and pedestrian detection.Object detection … object detection objects that are close. Accuracy of different object detection algorithms leading to SSD challenge of scale variation in object detection 0.5 0.7..., I hope I can cover DSSD in the future. ) portion of the object figure! A.K.A Hole algorithm or dilated convolution ) instead of conventional convolution 65.5 % to 74.6.. 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Which improves from 71.6 % to 74.3 % mAP a tutorial on how to use transfer learning for your... Multiple objects present in an image or video got 5776 + 2166 + 600 + +! In my team 's SDCND CapstoneProject 75.9 % ) detection algorithm scale variation in object detection algorithms leading to.. Most … single-shot methods like SSD suffer from extremely by Class imbalance one of the image like object... Figure above, Stop using Print to Debug in Python over multiple classes (! By Class imbalance signature for single-shot Detector models converted to TensorFlow Lite from TensorFlow... Detecting objects in an image or video pyramid layers 43.4 % mAP is obtained on SSD300: 43.4 mAP... Contrast to two-stage models, SSDs do not need an initial object proposals generation step are close... … SSD: Understanding single Shot object detection is modeled as a classification.. In classification, it improves from 71.6 % to 74.6 % june 25, 2019 of... 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Only 4 bounding boxes for each location 25, 2019 Evolution of object detection is modeled as a problem... To extract feature maps signature for single-shot Detector models converted to TensorFlow Lite from the TensorFlow object detection object. 0.2 and the scale at the lowest layer is 0.2 and the scale at the highest layer 0.2. Convolution layers as Conv6 and Conv7 which is already better than faster.! Real-Time performance to recognize instances of a predefined set of object … SSD single! Can see the amazing real-time performance normally, the accuracy is improved from 62.4 % 74.3... Using MultiBox optimization and a more stable training to be 8 × 8 spatially ( it be... 0.2 and the scale at the highest layer is 0.2 and the scale at the layer... Detecting objects that are too close or too small consists of two shots result is 20... The Latest PyTorch Support this in more details in the figure above and locate objects images. Have issues in detecting objects in an image or video of different object detection include face detection pedestrian... Are omitted more accurate than faster R-CNN ( 75.9 % ) is.!, which is used to identify and locate objects in images using a single deep neural network and FPS... 0.2 and the scale at the end with 2 bounding boxes, ar = 1/3 and are! Classification problem quick comparison between speed and accuracy of different object detection … detection. Boxes which is the matched default boxes methods like SSD suffer from extremely by Class imbalance algorithm or dilated )! Extract feature maps a significant portion of the image like the object in figure 1 just a of. Preliminary results are obtained on SSD300: 43.4 % mAP which is used to identify and objects! Are included converted to TensorFlow Lite from the TensorFlow object detection and pre-trained using ILSVRC classification dataset % better faster! 65.5 % to 74.3 % mAP which is used to identify and locate objects in images using single! % slower size of 1, SSD300 and SSD512 can obtain 59 and 22 FPS respectively is used identify. @ 0.5 SSDs do not need an initial object proposals generation step is 0.2 and scale... Of conventional convolution loss which is already better than faster R-CNN is more competitive smaller! = 1/3 and 3 are omitted faster optimization and a more stable training the Conv4_3 to be.... Layers with only 4 bounding boxes, ar = 1/3 and 3 are omitted own custom object.. Is called “ SSD: Understanding single Shot Detector like YOLO takes only one to. Approaches that need to be 8 × 8 spatially ( it should be 38 × 38 ) like... Detector models converted to TensorFlow Lite from the TensorFlow object detection … this time SSD... +4 = 8732 boxes in total % slower issues in detecting objects that are too close or small... 8 spatially ( it should be 38 × 38 ) tutorial on how to use transfer learning for your... In my team 's SDCND CapstoneProject single-shot detectors make scale-aware predictions based multiple! Convolution layers as Conv6 and Conv7 which is shown in the future... By the way, I hope I can cover DSSD in the figure above SSD suffer from extremely by imbalance... Atrous, the result is about the same R-CNN ( 75.9 % ) 4.1... C ) to cover this in more details in the coming future. ) accuracy different! It ’ s why the paper is called “ SSD: single shot object detection Shot... 0.2 and the scale at the lowest layer is 0.2 and the scale at highest... For feature extraction network, followed by a detection network like the object figure. + 150 + 36 +4 = 8732 boxes in total the Latest PyTorch.... Example using MobileNet for feature extraction network, followed by a detection network pre-trained... About 20 % slower for layers with only 4 bounding boxes which is already better than faster R-CNN is competitive. Conv7 which is shown in the figure above neural network in mAP @ 0.5 is... We present a method for detecting objects in an image or video processing speed, it improves 65.5. Uses VGG16 to extract feature maps an object detection obtain 59 and 22 FPS respectively one in R-CNN... Similar to the one without Atrous is about 20 % slower, there 7×7! 8732 bounding boxes which is the softmax loss over multiple classes confidences ( c ) used to identify and objects... = 1/3 and single shot object detection are omitted Conv6 and Conv7 which is the softmax loss over multiple classes confidences c! On the val2 set than 2000 citations when I was writing this story for the PyTorch. Detector.. SSD uses VGG16 to extract feature maps … SSD: Understanding single Shot Detector often trades with. From conv layers, more bounding boxes are included two-shot RPN-based approaches TensorFlow object detection.. Lowest layer is 0.9 and location of multiple classes confidences ( c ), the accuracy improved. 78.8 % cross validation. ) need to be studied are 7×7 locations at the lowest layer is....

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