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Smin is 0.2, Smax is 0.9. Authors believe it is due to the RPN-based approaches which consist of two shots. [ (maps) -359.019 (are) -357.992 (b) 20.0016 (uilt\054) -385.985 (learning) -358.982 (semantic) -359.014 (information) -358.014 (in) -358.989 (a) -359.004 (hierar) 19.9918 (\055) ] TJ [ (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 48.406 786.422 515.188 -52.699 re q /R253 306 0 R [ (block) -274.016 (can) -273.016 (prune) -273.986 (out) -272.996 (the) -273.986 (location) -272.984 (information\054) -279.988 (and) -273.008 (learn) -273.993 (the) ] TJ (\054) Tj T* /R33 9.9626 Tf 1 0 0 rg /Contents 280 0 R 44.532 4.33906 Td /R75 97 0 R -200.616 -11.9551 Td 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. (test\055dev) Tj /Type /Page T* q 93.966 4.33789 Td [ (\051\054) -253.997 (which) ] TJ >> /R61 89 0 R Earlier architectures for object detection consisted of two distinct stages – a region proposal network that performs object localization and a classifier for detecting the types of objects … -397.804 -18.2859 Td [ (\135\054) -279.992 (object) -274.018 (de\055) ] TJ Q That’s why the paper is called “SSD: Single Shot MultiBox Detector”. -112.519 -11.9563 Td 1 0 0 1 264.248 236.433 Tm 1 0 0 1 89.3746 236.433 Tm << /R30 32 0 R (\054) Tj /R172 228 0 R /x10 23 0 R /Group << [ (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 In this article, we propose a unified framework called … /R128 177 0 R endobj T* << /CA 1 /Rotate 0 6 0 obj Q /R33 9.9626 Tf Below is a SSD example using MobileNet for feature extraction: From above, we can see the amazing real-time performance. 10 0 0 10 0 0 cm /R31 14.3462 Tf BT /R73 95 0 R /R251 308 0 R 1 0 0 1 254.285 236.433 Tm [ (\056\054) -317.001 (no) -302.998 (e) 19.9918 (xtr) 14.9865 (a) -303.003 (annotation) -302.991 (is) -302.996 (r) 37.0183 (equir) 36.9938 (ed\056) -469.998 (In) -303.006 (con\055) ] TJ /Type /Group 1 0 0 1 350.217 250.139 Tm /Pages 1 0 R /R33 9.9626 Tf BT 11.9551 -13.7223 Td /ExtGState << /R33 9.9626 Tf [ (perfect) -250.005 (lo) 24.9885 (w) -249.995 (le) 25.0179 (v) 14.9828 (el) -249.995 (features\056) ] TJ /XObject << T* n >> 0 1 0 rg /Group << 0 1 0 rg 11.9551 TL /R33 9.9626 Tf 10 0 0 10 0 0 cm Q Make learning your daily ritual. /Resources << 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 … [ (sion) -250.013 (on) -249.988 (a) -250.002 (set) -249.988 (of) -249.996 (pre\055computed) -250.012 (anchors\056) ] TJ /R31 62 0 R (13) Tj >> /Rotate 0 /R83 99 0 R /R79 92 0 R 11.9551 TL [ (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 /Resources << /a0 << BT /R130 154 0 R SSD: Single Shot MultiBox Detectorより引用 (a)が入力画像と各物体の正解ボックスです。 (b)と(c)のマス目は特徴マップの位置を表しており、各位置においてデフォルトボックスと呼ばれる異なるアス … Q 10 0 0 10 0 0 cm ET /R94 136 0 R 4.23398 0 Td /x10 Do >> 9.68398 0 Td /Type /Group 10 0 0 10 0 0 cm q /R39 8.9664 Tf It is significantly faster in speed and high-accuracy object detection algorithm. /R31 62 0 R 11.9551 TL /R57 114 0 R /R33 11.9552 Tf (\054) Tj /R77 91 0 R ET /ca 1 q [ (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 /R31 11.9552 Tf << >> Q T* 11.9547 -11.9711 Td /R128 177 0 R Single Shot detector like YOLO takes only one shot to detect multiple objects present in an image using multibox. Object detection is modeled as a classification problem. endstream (10) Tj /Parent 1 0 R /R41 9.9626 Tf T* [ (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 BT ET >> By the way, I hope I can cover DSSD in the future. /R33 9.9626 Tf /R43 9.9626 Tf /R31 62 0 R From the MobileNet architecture, the last fully … /R35 7.9701 Tf [ (tection) -407.007 (datasets) -406.993 (demonstr) 15.011 (ate) -406.984 (the) -406.984 (ef) 18 (fectiveness) -407.013 (of) -407.998 (the) -406.984 (pr) 44.9851 (o\055) ] TJ Q /R261 300 0 R 0 1 0 rg q It is notintended to be a tutorial. Q 11.7461 0 Td Feature Selective Anchor-Free Module for Single-Shot Object Detection Chenchen Zhu, Yihui He, Marios Savvides We motivate and present feature selective anchor-free (FSAF) module, a … Q /R31 62 0 R Take a look, Stop Using Print to Debug in Python. [ (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 (test) Tj /R33 9.9626 Tf 0 g /R157 200 0 R /F2 9 Tf /R30 32 0 R stream /Contents 86 0 R 0 g 10 0 0 10 0 0 cm /R88 124 0 R >> 0 g 1 0 0 1 528.906 349.315 Tm /R120 173 0 R T* ET 10 0 0 10 0 0 cm /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /I true 151.785 0 Td Q q /I true ET /R37 44 0 R << /R84 102 0 R >> %PDF-1.3 >> I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. x�+��O4PH/VЯ04Up�� /R41 9.9626 Tf -15.0641 -11.9551 Td BT Single-Shot-Object-Detection-Updated From the Udemy Course on Open-CV by Hadelin de Ponteves. (5813) Tj /F1 244 0 R 10 0 0 10 0 0 cm /BBox [ 67 752 84 775 ] /R30 32 0 R 1 0 0 1 212.821 248.388 Tm x�+��O4PH/VЯ0�Pp�� /R72 93 0 R 1 0 0 1 182.455 248.388 Tm q /R35 7.9701 Tf This code includes the updated SSD Class for the Latest PyTorch Support. /R90 129 0 R /Resources 16 0 R /S /Transparency 1 0 0 1 237.966 248.388 Tm 36.677 -41.0461 Td /R122 170 0 R >> endstream -11.9547 -11.9551 Td /R94 136 0 R /R33 54 0 R /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] As you can see in the above image we are detecting coffee, iPhone, notebook, laptop … [ (It) -315.982 (consists) -315.986 (of) -315.004 (se) 25.0179 (v) 14.9828 (eral) -315.991 (global) -316.001 (acti) 24.9811 (v) 24.9811 (ation) -316.006 (blocks\054) -331.999 (as) -315.986 (sho) 24.9909 (wn) -316.016 (in) ] TJ /R33 9.9626 Tf The loss function consists of two terms: Lconf and Lloc where N is … [ (tion\056) -506.986 (It) -315.982 (tak) 10.0057 (es) -315.004 (the) -316.018 (lo) 24.9885 (w) -315.984 (le) 25.0179 (v) 14.9828 (el) -315.001 (detection) -316.001 (feature) -315.011 (map) -316.011 (as) -315.986 (input\054) ] TJ /Parent 1 0 R /a0 << 10 0 0 10 0 0 cm /x8 Do T* /Title (Single\055Shot Object Detection With Enriched Semantics) /R31 62 0 R 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. stream (18) Tj q /ExtGState << [ (tation) -280.985 (proces) 0.98513 (s\056) -402.002 (After) -281.012 (the) -279.985 (original) -280.983 (lo) 24.986 (w) -279.988 (le) 25.0203 (v) 14.9828 (el) -281.007 (features) -279.992 (\050B\051) -281.007 (are) ] TJ /Shading << /Contents 321 0 R BT q /Filter /FlateDecode T* [ (such) -273.982 (as) -273.992 (image) -274.017 <636c6173736902636174696f6e> -273.005 (\133) ] TJ [ (ferent) -250 (layers\056) -310.017 (This) -250.015 (is) -249.985 (sho) 24.9934 (wn) -249.988 (in) -249.988 (the) -249.988 (upper) -249.988 (part) -249.993 (of) -249.997 (Figure) ] TJ /R86 141 0 R 1 0 0 1 240.766 188.612 Tm /XObject << /R33 9.9626 Tf /R75 97 0 R T* 10 0 0 10 0 0 cm Q q /Pattern << ET (\054) Tj /ExtGState << Boxes which is used to identify and locate objects in images using a single deep network... 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Predictions based on multiple pyramid layers … single-shot methods like SSD suffer from extremely by imbalance!

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