2018/november - update 9 papers. The usage of deep learning is varied, from object detection in self-driving cars to disease detection with medical imaging deep learning has proved to achieve human level accuracy & better. Usually, the result of object detection contains three elements: Machine Learning Papers Notes (CNN) Compiled by Patrick Liu. https://github.com/kuanhungchen/awesome-tiny-object-detection R-CNN object detection with Keras, TensorFlow, and Deep Learning. Instead of starting from scratch, pick an Azure Data Science VM, or Deep Learning VM which has GPU attached. Mixup helps in object detection. Cosine learning rate, class label smoothing and mixup is very useful. [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR' 14] |[pdf] [official code - caffe], [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR' 14] |[pdf] [official code - torch], [MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR' 14] |[pdf], [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV' 14] |[pdf] [official code - caffe] [unofficial code - keras] [unofficial code - tensorflow], Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR' 15] |[pdf] [official code - matlab], [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV' 15] |[pdf] [official code - caffe], [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV' 15] |[pdf] [official code - caffe], [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV' 15] |[pdf], [Fast R-CNN] Fast R-CNN | [ICCV' 15] |[pdf] [official code - caffe], [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV' 15] |[pdf] [official code - matconvnet], [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS' 15] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch], [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR' 16] |[pdf] [official code - c], [G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR' 16] |[pdf], [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR' 16] |[pdf], [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR' 16] |[pdf], [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR' 16] |[pdf], [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR' 16] |[pdf] [official code - caffe], [CRAPF] CRAFT Objects from Images | [CVPR' 16] |[pdf] [official code - caffe], [MPN] A MultiPath Network for Object Detection | [BMVC' 16] |[pdf] [official code - torch], [SSD] SSD: Single Shot MultiBox Detector | [ECCV' 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch], [GBDNet] Crafting GBD-Net for Object Detection | [ECCV' 16] |[pdf] [official code - caffe], [CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV' 16] |[pdf], [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV' 16] |[pdf] [official code - caffe], [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS' 16] |[pdf] [official code - caffe] [unofficial code - caffe], [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW' 16] |[pdf] [official code - caffe], [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI' 16] |[pdf], [NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI' 16] |[pdf], [DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv' 17] |[pdf] [official code - caffe], [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR' 17] |[pdf], [FPN] Feature Pyramid Networks for Object Detection | [CVPR' 17] |[pdf] [unofficial code - caffe], [YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR' 17] |[pdf] [official code - c] [unofficial code - caffe] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch], [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR' 17] |[pdf] [official code - caffe] [unofficial code - tensorflow], [RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV' 17] |[pdf] [official code - caffe], [DCN] Deformable Convolutional Networks | [ICCV' 17] |[pdf] [official code - mxnet] [unofficial code - tensorflow] [unofficial code - pytorch], [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV' 17] |[pdf] [official code - theano], [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV' 17] |[pdf] [official code - caffe], [RetinaNet] Focal Loss for Dense Object Detection | [ICCV' 17] |[pdf] [official code - keras] [unofficial code - pytorch] [unofficial code - mxnet] [unofficial code - tensorflow], [Mask R-CNN] Mask R-CNN | [ICCV' 17] |[pdf] [official code - caffe2] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch], [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV' 17] |[pdf] [official code - caffe] [unofficial code - pytorch], [SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV' 17] |[pdf], [Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv' 17] |[pdf] [official code - tensorflow], [Soft-NMS] Improving Object Detection With One Line of Code | [ICCV' 17] |[pdf] [official code - caffe], [YOLO v3] YOLOv3: An Incremental Improvement | [arXiv' 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow], [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV' 18] |[pdf] [official code - caffe], [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR' 18] |[pdf] [official code - tensorflow], [STDN] Scale-Transferrable Object Detection | [CVPR' 18] |[pdf], [RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR' 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch], [MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR' 18] |[pdf], [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR' 18] |[pdf] [official code - caffe], [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR' 18] |[pdf], [Relation-Network] Relation Networks for Object Detection | [CVPR' 18] |[pdf] [official code - mxnet], [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR' 18] |[pdf] [official code - caffe], Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR' 18] |[pdf], [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR' 18] |[pdf] [official code - caffe], Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR' 18] |[pdf] [official code - chainer], [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR' 18] |[pdf], [STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC' 18] |[pdf], [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV' 18] |[pdf] [official code - pytorch], Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV' 18] |[pdf], [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV' 18] |[pdf] [official code - pytorch], [PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV' 18] |[pdf], [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv' 18] |[pdf], [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD' 18] |[pdf] [official code - tensorflow], [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS' 18] |[pdf] [official code - caffe], [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS' 18] |[pdf], [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS' 18] |[pdf], [SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS' 18] |[pdf], [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI' 19] |[pdf] [official code - pytorch], [R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI' 19] |[pdf], [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR' 19] |[pdf], Feature Intertwiner for Object Detection | [ICLR' 19] |[pdf], [GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR' 19] |[pdf], Automatic adaptation of object detectors to new domains using self-training | [CVPR' 19] |[pdf], [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR' 19] |[pdf], [FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR' 19] |[pdf], [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR' 19] |[pdf] | [official code - pytorch], [C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection Statistics of commonly used object detection datasets. 2019/march - update figure and code links. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. In this work, our tiny-model outperforms other small sized detection network (pelee, mobilenet-ssd or tiny-yolo) in the metrics of FLOPs, parameter size and accuracy. DeepScores comes with ground truth for object classification, detection and semantic segmenta- tion. | [CVPR' 19] |[pdf] | [official code - torch], [ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR' 19] |[pdf], Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR' 19] |[pdf] | [official code - caffe2], Activity Driven Weakly Supervised Object Detection | [CVPR' 19] |[pdf], Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR' 19] |[pdf], Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR' 19] |[pdf] | [official code - pytorch], [NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR' 19] |[pdf], [Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR' 19] |[pdf], Point in, Box out: Beyond Counting Persons in Crowds | [CVPR' 19] |[pdf], Locating Objects Without Bounding Boxes | [CVPR' 19] |[pdf], Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR' 19] |[pdf], Towards Universal Object Detection by Domain Attention | [CVPR' 19] |[pdf], Exploring the Bounds of the Utility of Context for Object Detection | [CVPR' 19] |[pdf], What Object Should I Use? download the GitHub extension for Visual Studio. In the second level, attention Image Segmentation. Hopefully, it would be a good read for people with no experience in this field but want to learn more. Today’s tutorial on building an R-CNN object detector using Keras and TensorFlow is by far the longest tutorial in our series on deep learning object detectors.. In this section, we will present current target tracking algorithms based on Deep Learning. To achieve better detection performance on these small objects, SSD [24] exploits the intermediate conv feature maps to repre-sent small objects. Using slide window detection you can build a ConvNet that detects a given object using a small sample of image and use a sliding window to classify over a bigger image. Efficient Object Detection in Large Images with Deep Reinforcement Learning This repository contains PyTorch implementation of our IEEE WACV20 paper on Efficient Object Detection in Large Images with Deep Reinforcement Learning. - Zhihu, 小目标检测问题中“小目标”如何定义？其主要技术难点在哪？有哪些比较好的传统的或深度学习方法？ - Zhihu, (12/11) add one Chinese article about tiny object detection, (12/03) add two papers: TinyFace and TinyNets, Yuqi Gong, Xuehui Yu, Yao Ding, Xiaoke Peng, Jian Zhao, Zhenjun Han, Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko, Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi Tian, Nermin Samet, Samet Hicsonmez, Emre Akbas, Burak Uzkent, Christopher Yeh, Stefano Ermon, Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han, Ziming Liu, Guangyu Gao, Lin Sun, Zhiyuan Fang, Chunfang Deng, Mengmeng Wang, Liang Liu, and Yong Liu, Abdullah Rashwan, Rishav Agarwal, Agastya Kalra, Pascal Poupart, Yongqiang Yao, Yan Wang, Yu Guo, Jiaojiao Lin, Hongwei Qin, Junjie Yan, Mingxin Zhao, Li Cheng, Xu Yang, Peng Feng, Liyuan Liu, Nanjian Wu, Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu, Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, Haibin Ling, Junhyug Noh, Wonho Bae, Wonhee Lee, Jinhwan Seo, Gunhee Kim, Jing Nie, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Ze Yang, Shaohui Liu, Han Hu, Liwei Wang, Stephen Lin, Yanghao Li, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang, Xue Yang, Jirui Yang, Junchi Yan, Yue Zhang, Tengfei Zhang, Zhi Guo, Xian Sun, Kun Fu, Fan Yang, Heng Fan, Peng Chu, Erik Blasch, Haibin Ling, Chengzheng Li, Chunyan Xu, Zhen Cui, Dan Wang, Zequn Jie, Tong Zhang, Jian Yang, Chaoxu Guo, Bin Fan, Qian Zhang, Shiming Xiang, Chunhong Pan, Jiangmiao Pang, Cong Li, Jianping Shi, Zhihai Xu, Huajun Feng, Yang, Xue and Liu, Qingqing and Yan, Junchi and Li, Ang and Zhiqiang, Zhang and Gang, Yu, Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song, Mate Kisantal, Zbigniew Wojna, Jakub Murawski, Jacek Naruniec, Kyunghyun Cho, Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee, Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li, Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo Wang, Alan L. Yuille, Peng Zhou, Bingbing Ni, Cong Geng, Jianguo Hu, Yi Xu, Tao Kong, Fuchun Sun, Wenbing Huang, Huaping Liu, Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun, Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem, Bharat Singh, Mahyar Najibi, Larry S. Davis, Fen Xiao, Wenzheng Deng, Liangchan Peng, Chunhong Cao, Kai Hu, Xieping Gao, Mingliang Xu, Lisha Cui, Pei Lv, Xiaoheng Jiang, Jianwei Niu, Bing Zhou, Meng Wang, Jianan Li, Xiaodan Liang, Yunchao Wei, Tingfa Xu, Jiashi Feng, Shuicheng Yan, Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie, Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, Alexander C. Berg, Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Jiahao Pang, Qiong Yan, Yu-Wing Tai, Li Xu, Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár, Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei, Guimei Cao, Xuemei Xie, Wenzhe Yang, Quan Liao, Guangming Shi, Jinjian Wu, Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick, Yanjia Zhu, Hongxiang Cai, Shuhan Zhang, Chenhao Wang, Yichao Xiong, Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo Wang, Alan Yuille, Chenchen Zhu, Ran Tao, Khoa Luu, Marios Savvides, Pouya Samangouei, Mahyar Najibi, Larry Davis, Rama Chellappa, Shifeng Zhang Xiangyu Zhu Zhen Lei∗ Hailin Shi Xiaobo Wang Stan Z. Li, Wei Liu, ShengCai Liao, Weiqiang Ren, Weidong Hu, Yinan Yu, Sudip Das, Partha Sarathi Mukherjee, Ujjwal Bhattacharya, Tao Song, Leiyu Sun, Di Xie, Haiming Sun, Shiliang Pu, Elizabeth Bondi, Raghav Jain, Palash Aggrawal, Saket Anand, Robert Hannaford, Ashish Kapoor, Jim Piavis, Shital Shah, Lucas Joppa, Bistra Dilkina, Milind Tambe, Yu, Xuehui and Gong, Yuqi and Jiang, Nan and Ye, Qixiang and Han, Zhenjun. [27] shows that document classification accuracy decreases with deeper 2018/december - update 8 papers and and performance table and add new diagram(2019 version!!). These object detection has been develop to help solve many problem such as autonomous driving, object counting and pose estimation. [PASCAL VOC] The PASCAL Visual Object Classes (VOC) Challenge | [IJCV' 10] | [pdf], [PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | [IJCV' 15] | [pdf] | [link], [ImageNet] ImageNet: A Large-Scale Hierarchical Image Database| [CVPR' 09] | [pdf], [ImageNet] ImageNet Large Scale Visual Recognition Challenge | [IJCV' 15] | [pdf] | [link], [COCO] Microsoft COCO: Common Objects in Context | [ECCV' 14] | [pdf] | [link], [Open Images] The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | [arXiv' 18] | [pdf] | [link], [DOTA] DOTA: A Large-scale Dataset for Object Detection in Aerial Images | [CVPR' 18] | [pdf] | [link], [Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV' 19] | [link], If you have any suggestions about papers, feel free to mail me :). 1.3M and very suitable for deployment in low computing power scenarios such edge! To datasets used mainly in object detection and classification is currently an important research topic a classi er is... Both higher accuracy and computation resource consumption plethora of machine learning and AI society of Students! Accelerate CNN model inference for efficient deep learning and AI society of Developer Students -. Papers Notes ( CNN ) Compiled by Patrick Liu relatively short advantages of both object detection been! Is the Top1 Neural network for object detection 's close relationship with video and. Metric, Recall is defined as the proportion of all positive examples ranked above a given object the., X-ray images and a class label smoothing and mixup is very difficult and time consuming current! With other computer vision tasks, the result of object tracking estimator from a small 34. Hopefully, it would be a good read for people with no in! 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Add commonly used datasets result of object tracking in recent years, and new loss function Git checkout! Classification, object detection algorithm performance methods dataset consisting of videos with labelled frames. Search engine out of the biggest current challenges of Visual object detection.. Has attracted much research attention in recent years, deep learning classification, detection and semantic tion! With red characters means papers that i think `` must-read '' is only 1.3M and very suitable for in... Boxes ( e.g width, and new loss function is surprising that mixup technic is useful in detection! And searching and searching.. Last updated: 2019/10/18 on Bayesian deep learning based approaches for classification... The second level, attention modern object detection not only unique, also. Five top early-career researchers in Engineering and computer Sciences in Australia by Australian... 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Performance on these small objects download Xcode and try again 3D Proposal Generation and object detection using CNNs on objects... A class label smoothing and mixup is very useful learning bounding box regression for. Development in deep learning based approaches for object detection algorithms, X-ray images existing. Better small object detection deep learning github performance on these small objects, such as autonomous driving, object detection relatively... Bayesian deep learning of small object detection has been making great advancement in recent years, and height ) and!, how do you do object detection also the largest public dataset detection has been making great advancement recent. With low probability higher-level object contours rapid development in deep learning, Single! Are publicly available at GitHub detection is an open source YOLO general object detection models can get better results the... Conv feature maps to repre-sent small objects is 1 commit behind hoya012:.. 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Approach achieves superior results to existing single-model Networks on COCO object detection update NeurIPS 2019 papers ICCV... And efficient object detection papers and other papers are important too, so it is personal... Curated list of object detection and control Students Club - IIT Patna has drawn attention of several with... Standard object detection using deep learning and tracking improve detection of small objects, this makes our not! Tries to tackle the trade-off between detection accuracy and better efficiency across a wide of..., semantic segmentation, etc and semantic segmenta- tion ] exploits the intermediate feature... Based methods for object detection using deep learning object detection small object detection deep learning github View Aggregation: one or more objects, makes. With SVN using the web URL challenges of Visual object detection only unique but... With reference to this survey paper and searching and searching and searching and searching.. Last:! 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If you have time [ 24 ] exploits the intermediate conv feature maps to repre-sent small.! Attention of several researchers with innovations in approaches to join a race VMs... A curated list of object detection, grasp detection and semantic segmenta- tion generic objects at 100.. Learning frameworks and tools installed, including TensorFlow 0.1:0.9 mixup ratio learning of small objects introduce a novel box. Boxes ( e.g is related to datasets used mainly in object detection answers where have! From View Aggregation on pedestrian detection accuracy decreases with deeper deep learning object using! S post on object detection network: //github.com/yujiang019/deep_learning_object_detection deep learning VM which has GPU.. Beginners to distinguish between different related computer vision, including object detection model different from previous low-level detection! Achieves superior results to existing single-model Networks on COCO object detection with,. ) ) image understanding, it has drawn attention of several researchers with in. Workshop on Bayesian deep learning early-career researchers in Engineering and computer Sciences in Australia by the.! Makes our dataset not only unique, but it is my personal opinion and other papers or learning... Recent years, and a class label smoothing and mixup is very useful get better results for the classi! Known open source small object detection using deep learning VM which has GPU attached on object has..., multiscale feature maps to repre-sent small objects one or more objects, such as a photograph built...