Classification / Recognition

Papers

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

• auothor: Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
• arxiv: http://arxiv.org/abs/1310.1531

CNN Features off-the-shelf: an Astounding Baseline for Recognition

• intro: CVPR 2014
• arxiv: http://arxiv.org/abs/1403.6382

HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification

HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition

• intro: ICCV 2015
• intro: introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy
• project page: https://sites.google.com/site/homepagezhichengyan/home/hdcnn
• arxiv: https://arxiv.org/abs/1410.0736
• code: https://sites.google.com/site/homepagezhichengyan/home/hdcnn/code
• github: https://github.com/stephenyan1231/caffe-public/tree/hdcnn

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

• intro: ImageNet top-5 error: 4.94%
• arxiv: http://arxiv.org/abs/1502.01852
• notes: http://blog.csdn.net/happynear/article/details/45440811

Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1511.05520
• github: https://github.com/glennq/instrument-recognition

Deep Convolutional Networks on the Pitch Spiral for Musical Instrument Recognition

• paper: https://github.com/lostanlen/ismir2016/blob/master/paper/lostanlen_ismir2016.pdf
• github: https://github.com/lostanlen/ismir2016

Humans and deep networks largely agree on which kinds of variation make object recognition harder

• arxiv: http://arxiv.org/abs/1604.06486
• review: https://www.technologyreview.com/s/601387/why-machine-vision-is-flawed-in-the-same-way-as-human-vision/

FusionNet: 3D Object Classification Using Multiple Data Representations

• arxiv: https://arxiv.org/abs/1607.05695

From image recognition to object recognition

• blog: https://www.oreilly.com/ideas/from-image-recognition-to-object-recognition

Deep FisherNet for Object Classification

• arxiv: http://arxiv.org/abs/1608.00182

Factorized Bilinear Models for Image Recognition

• intro: TuSimple
• arxiv: https://arxiv.org/abs/1611.05709
• github(MXNet): https://github.com/lyttonhao/Factorized-Bilinear-Network

Hyperspectral CNN Classification with Limited Training Samples

• arxiv: https://arxiv.org/abs/1611.09007

The More You Know: Using Knowledge Graphs for Image Classification

• intro: CMU. GSNN
• arxiv: https://arxiv.org/abs/1612.04844

MaxMin Convolutional Neural Networks for Image Classification

• paper: http://webia.lip6.fr/~thomen/papers/Blot_ICIP_2016.pdf
• github: https://github.com/karandesai-96/maxmin-cnn

Cost-Effective Active Learning for Deep Image Classification

• intro: TCSVT 2016
• intro: Sun Yat-sen University & Guangzhou University
• arxiv: https://arxiv.org/abs/1701.03551

Deep Collaborative Learning for Visual Recognition

https://www.arxiv.org/abs/1703.01229

Convolutional Low-Resolution Fine-Grained Classification

https://arxiv.org/abs/1703.05393

Multi-Scale Dense Networks for Resource Efficient Image Classification

• intro: Cornell University & Fudan University & Tsinghua University & Facebook AI Research
• arxiv: https://arxiv.org/abs/1703.09844
• github: https://github.com//gaohuang/MSDNet

Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

https://arxiv.org/abs/1706.07901

Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

• intro: BMVC 2017
• arxiv: https://arxiv.org/abs/1707.06335

Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification

• intro: Poster presented at CVPR 2017 Scene Understanding Workshop
• arxiv: https://arxiv.org/abs/1709.03439

B-CNN: Branch Convolutional Neural Network for Hierarchical Classification

https://arxiv.org/abs/1709.09890

Learning Transferable Architectures for Scalable Image Recognition

• intro: Google Brain
• keywords: Neural Architecture Search
• arxiv: https://arxiv.org/abs/1707.07012

AOGNets: Deep AND-OR Grammar Networks for Visual Recognition

https://arxiv.org/abs/1711.05847

Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN

• intro: University of Southern California & Google Research
• arxiv: https://arxiv.org/abs/1711.07607

Between-class Learning for Image Classification

• intro: The University of Tokyo & RIKEN
• arxiv: https://arxiv.org/abs/1711.10284

Efficient Traffic-Sign Recognition with Scale-aware CNN

• intro: BMVC 2017
• arxiv: https://arxiv.org/abs/1805.12289

Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition

• intro: AAAI 2018
• arix:vhttps://arxiv.org/abs/1712.01907

µNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification

https://arxiv.org/abs/1804.00497

Deep Predictive Coding Network for Object Recognition

https://arxiv.org/abs/1802.04762

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

• intro: CVPR 2018. The Robotics Institute, Carnegie Mellon University
• arxiv: https://arxiv.org/abs/1803.08035

Attention-based Pyramid Aggregation Network for Visual Place Recognition

• intro: ACM MM 2018
• arxiv: https://arxiv.org/abs/1808.00288

How do Convolutional Neural Networks Learn Design?

• intro: ICPR 2018
• arxiv: https://arxiv.org/abs/1808.08402

Making Classification Competitive for Deep Metric Learning

https://arxiv.org/abs/1811.12649

In Defense of the Triplet Loss for Visual Recognition

• intro: University of Maryland & Honda Research Institute
• arxiv: https://arxiv.org/abs/1901.08616

All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1903.05285

Deep CNN-based Multi-task Learning for Open-Set Recognition

https://arxiv.org/abs/1903.03161

Squared Earth Mover’s Distance-based Loss for Training Deep Neural Networks

https://arxiv.org/abs/1611.05916

Large-Scale Long-Tailed Recognition in an Open World

• intro: CVPR 2019 oral
• intro: CUHK & UC Berkeley / ICSI
• project page: https://liuziwei7.github.io/projects/LongTail.html
• arxiv: https://arxiv.org/abs/1904.05160

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

• intro: Google Research, Brain Team
• arxiv: https://arxiv.org/abs/2010.11929
• github: https://github.com/google-research/vision_transformer

High-Performance Large-Scale Image Recognition Without Normalization

• intro: NFNet
• arxiv: https://arxiv.org/abs/2102.06171
• github: https://github.com/deepmind/deepmind-research/tree/master/nfnets

Massive Classification

Accelerated Training for Massive Classification via Dynamic Class Selection

• intro: AAAI 2018. CUHK & SenseTime
• keywords: HF-Softmax
• arxiv: https://arxiv.org/abs/1801.01687
• github: https://github.com/yl-1993/hfsoftmax

Multi-object Recognition

Multiple Object Recognition with Visual Attention

• keyword: deep recurrent neural network, reinforcement learning
• arxiv: https://arxiv.org/abs/1412.7755
• github: https://github.com/jrbtaylor/visual-attention

Multiple Instance Learning Convolutional Neural Networks for Object Recognition

• intro: ICPR 2016 Oral
• arxiv: https://arxiv.org/abs/1610.03155

Multi-Label Classification

Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification

• intro: CVPR 2017
• intro: University of Science and Technology of China & CUHK
• arxiv: https://arxiv.org/abs/1702.05891
• github(official. Caffe): https://github.com/zhufengx/SRN_multilabel/

Order-Free RNN with Visual Attention for Multi-Label Classification

https://arxiv.org/abs/1707.05495

Learning Social Image Embedding with Deep Multimodal Attention Networks

• intro: Beihang University & Microsoft Research
• arxiv: https://arxiv.org/abs/1710.06582

Multi-label Image Recognition by Recurrently Discovering Attentional Regions

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1711.02816

Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1712.07465

A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN

https://arxiv.org/abs/1811.08412

Multi-class Classification without Multi-class Labels

• intro: ICLR 2019
• arxiv: https://arxiv.org/abs/1901.00544

Learning a Deep ConvNet for Multi-label Classification with Partial Labels

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1902.09720

Multi-Label Image Recognition with Graph Convolutional Networks

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.03582
• github: https://github.com/chenzhaomin123/ML_GCN

General Multi-label Image Classification with Transformers

• intro: University of Virginia
• arxiv: https://arxiv.org/abs/2011.14027

Person Recognition

Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues

• intro: UC Berkeley & Facebook AI Research
• keywords: People In Photo Albums (PIPA) dataset, Pose Invariant PErson Recognition (PIPER)
• project page: https://people.eecs.berkeley.edu/~nzhang/piper.html
• arxiv: https://arxiv.org/abs/1501.05703

COCO_v1

Learning Deep Features via Congenerous Cosine Loss for Person Recognition

• keywords: COCO loss
• arxiv: https://arxiv.org/abs/1702.06890
• github: https://github.com/sciencefans/coco_loss

Pose-Aware Person Recognition

• intro: CVIT & Facebook AI Research
• arxiv: https://arxiv.org/abs/1705.10120

COCO_v2

Rethinking Feature Discrimination and Polymerization for Large-scale Recognition

• intro: NIPS 2017 Deep Learning Workshop
• keywords: COCO loss
• arxiv: https://arxiv.org/abs/1710.00870
• github: https://github.com/sciencefans/coco_loss

Person Recognition in Social Media Photos

https://arxiv.org/abs/1710.03224

Unifying Identification and Context Learning for Person Recognition

• intro: CVPR 2018
• paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Unifying_Identification_and_CVPR_2018_paper.pdf

Fine-grained Recognition

Bilinear CNN Models for Fine-grained Visual Recognition

• intro: ICCV 2015
• homepage: http://vis-www.cs.umass.edu/bcnn/
• paper: http://vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf
• arxiv: http://arxiv.org/abs/1504.07889
• bitbucket: https://bitbucket.org/tsungyu/bcnn.git

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

• intro: CVPR 2016
• project page: http://www.f-zhou.com/fg.html
• arxiv: http://arxiv.org/abs/1512.02665
• demo: http://www.f-zhou.com/fg_demo/

Embedding Label Structures for Fine-Grained Feature Representation

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1512.02895
• paper: http://webpages.uncc.edu/~szhang16/paper/CVPR16_structured_labels.pdf

Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop

• arxiv: http://arxiv.org/abs/1512.05227

Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition

• arxiv: http://arxiv.org/abs/1603.06765

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

• arxiv: https://arxiv.org/abs/1605.06217

Learning Deep Representations of Fine-grained Visual Descriptions

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1605.05395
• github: https://github.com/reedscot/cvpr2016

IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1606.03238

Picking Deep Filter Responses for Fine-grained Image Recognition

• intro: CVPR 2016

SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition

• intro: CVPR 2016

Part-Stacked CNN for Fine-Grained Visual Categorization

• intro: CVPR 2016

Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches

• intro: BMVC 2016
• arxiv: https://arxiv.org/abs/1610.06756

Low-rank Bilinear Pooling for Fine-Grained Classification

• intro: CVPR 2017
• project page: http://www.ics.uci.edu/~skong2/lr_bilinear.html
• arxiv: https://arxiv.org/abs/1611.05109
• github: https://github.com/aimerykong/Low-Rank-Bilinear-Pooling

细粒度图像分析

• intro: by 吴建鑫, NJU. VALSE 2017 Annual Progress Review Series
• slides: http://mac.xmu.edu.cn/valse2017/ppt/APR/wjx_APR.pdf

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-grained Image Recognition

• intro: CVPR 2017
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Look_Closer_to_CVPR_2017_paper.pdf

Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1709.02476

Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition

https://arxiv.org/abs/1709.05769

Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition

• introL ICCV 2017
• keywords: MA-CNN
• intro: University of Science and Technology of China & Microsoft Research & University of Rochester
• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Learning_Multi-Attention_Convolutional_ICCV_2017_paper.pdf

TransFG: A Transformer Architecture for Fine-grained Recognition

• intro: Johns Hopkins University & ByteDance Inc.
• arxiv: https://arxiv.org/abs/2103.07976

Food Recognition

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

• arxiv: http://arxiv.org/abs/1606.05675
• github: https://github.com/deercoder/DeepFood

Im2Calories: towards an automated mobile vision food diary

• intro: recognize the contents of your meal from a single image, then predict its nutritional contents, such as calories
• paper: http://www.cs.ubc.ca/~murphyk/Papers/im2calories_iccv15.pdf

Food Image Recognition by Using Convolutional Neural Networks (CNNs)

• arxiv: https://arxiv.org/abs/1612.00983

Wide-Slice Residual Networks for Food Recognition

• arxiv: https://arxiv.org/abs/1612.06543

Food Classification with Deep Learning in Keras / Tensorflow

• blog: http://blog.stratospark.com/deep-learning-applied-food-classification-deep-learning-keras.html
• github: https://github.com/stratospark/food-101-keras

ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition

https://arxiv.org/abs/1705.02743

Computer vision-based food calorie estimation: dataset, method, and experiment

https://arxiv.org/abs/1705.07632

Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

https://arxiv.org/abs/1706.04062

Food Ingredients Recognition through Multi-label Learning

https://arxiv.org/abs/1707.08816

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

• intro: IEEE Signal Processing Letters
• arxiv: https://arxiv.org/abs/1709.09429

Food recognition and recipe analysis: integrating visual content, context and external knowledge

https://arxiv.org/abs/1801.07230

Attribute Recognition

Multi-task CNN Model for Attribute Prediction

• intro: ieee transaction paper
• arxiv: https://arxiv.org/abs/1601.00400

Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification

https://arxiv.org/abs/1604.07360

Generative Adversarial Models for People Attribute Recognition in Surveillance

• intro: AVSS 2017 oral
• arxiv: https://arxiv.org/abs/1707.02240

Attribute Recognition by Joint Recurrent Learning of Context and Correlation

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1709.08553

Multi-label Object Attribute Classification using a Convolutional Neural Network

https://arxiv.org/abs/1811.04309

Pedestrian Attribute Recognition / Person Attribute Recognition

Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios

• intro: ACPR 2015
• keywords: DeepSAR / DeepMAR
• paper: http://or.nsfc.gov.cn/bitstream/00001903-5/417802/1/1000014103914.pdf
• github: https://github.com/kyu-sz/DeepMAR_deploy
• github: https://github.com/dangweili/pedestrian-attribute-recognition-pytorch

Pedestrian Attribute Recognition At Far Distance

• intro: ACM MM 2014
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/mm14.pdf

Person Attribute Recognition with a Jointly-trained Holistic CNN Model

• intro: ICCV 2015
• keywords: Parse27k
• arxiv: https://www.vision.rwth-aachen.de/media/papers/sudowe_spitzer_leibe_ICCV_LaP_2015.pdf

Human Attribute Recognition by Deep Hierarchical Contexts

• intro: ECCV 2016
• paper: http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2016_human.pdf

Robust Pedestrian Attribute Recognition for an Unbalanced Dataset using Mini-batch Training with Rarity Rate

• intro: Intelligent Vehicles Symposium 2016
• intro: Chubu University & Nagoya University, Japan
• paper: http://www.vision.cs.chubu.ac.jp/MPRG/C_group/C081_fukui2016.pdf

Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization

• arxiv: https://arxiv.org/abs/1611.05603
• github: https://github.com/kyu-sz/WPAL-network

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

• intro: BMVC 2017
• keywords: PETA, RAP and WIDER
• arxiv: https://arxiv.org/abs/1707.06089
• github: https://github.com/asc-kit/vespa

HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

• intro: ICCV 2017
• intro: CUHK & SenseTime
• keywords: multi-directional attention (MDA)
• arxiv: https://arxiv.org/abs/1709.09930
• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.pdf
• github: https://github.com/xh-liu/HydraPlus-Net

Deep Imbalanced Attribute Classification using Visual Attention Aggregation

• intro: ECCV 2018
• intro: University of Houston
• arxiv: https://arxiv.org/abs/1807.03903

Localization Guided Learning for Pedestrian Attribute Recognition

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1808.09102

Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning

• intro: IJCAI 2018
• paper: https://www.ijcai.org/proceedings/2018/0441.pdf

Sequence-based Person Attribute Recognition with Joint CTC-Attention Model

• keywords: joint CTC-Attention model (JCM), s connectionist temporal classification (CTC)
• arxiv: https://arxiv.org/abs/1811.08115

The Deeper, the Better: Analysis of Person Attributes Recognition

https://arxiv.org/abs/1901.03756

Video-Based Pedestrian Attribute Recognition

https://arxiv.org/abs/1901.05742

Pedestrian Attribute Recognition: A Survey

• intro: Anhui University
• project page: https://sites.google.com/view/ahu-pedestrianattributes/
• arxiv: https://arxiv.org/abs/1901.07474

Papers with code: Pedestrian Attribute Recognition

https://paperswithcode.com/task/pedestrian-attribute-recognition/codeless

Pedestrian-Attribute-Recognition-Paper-List

https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List

Attribute Aware Pooling for Pedestrian Attribute Recognition

• intro: IJCAI 2019
• intro: Huawei Noah’s Ark Lab & University of Sydney
• arxiv: https://arxiv.org/abs/1907.11837

Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism

• intro: AAAI 2020 oral
• arxiv: https://arxiv.org/abs/1911.11351

Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method

• intro: University of Chinese Academy of Sciences & Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/2005.11909
• github(official, Pytorch): https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition

Hierarchical Feature Embedding for Attribute Recognition

• intro: SenseTime Group Limited & Tsinghua University
• arxiv: https://arxiv.org/abs/2005.11576

Clothes Recognition

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

• intro: CVPR 2016
• keywords: FashionNet
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf

Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes

• arxiv: https://arxiv.org/abs/1610.03670

Star-galaxy Classification

Star-galaxy Classification Using Deep Convolutional Neural Networks

• intro: MNRAS
• arxiv: http://arxiv.org/abs/1608.04369
• github: https://github.com/EdwardJKim/dl4astro

Logo Recognition

Deep Learning for Logo Recognition

• arxiv: https://arxiv.org/abs/1701.02620

Plant Classification

Large-Scale Plant Classification with Deep Neural Networks

• intro: Published at Proocedings of ACM Computing Frontiers Conference 2017
• arxiv: https://arxiv.org/abs/1706.03736

Scene Recognition / Scene Classification

Learning Deep Features for Scene Recognition using Places Database

• paper: http://places.csail.mit.edu/places_NIPS14.pdf
• gihtub: https://github.com/metalbubble/places365

Using neon for Scene Recognition: Mini-Places2

• intro: This is an implementation of the deep residual network used for Mini-Places2 as described in He et. al., “Deep Residual Learning for Image Recognition”.
• blog: http://www.nervanasys.com/using-neon-for-scene-recognition-mini-places2/
• github: https://github.com/hunterlang/mpmz

Scene Classification with Inception-7

• slides: http://lsun.cs.princeton.edu/slides/Christian.pdf

Semantic Clustering for Robust Fine-Grained Scene Recognition

• arxiv: http://arxiv.org/abs/1607.07614

Scene recognition with CNNs: objects, scales and dataset bias

• intro: CVPR 2016
• arxiv: https://arxiv.org/abs/1801.06867

Leaderboard

Leaderboard of Places Database

• intro: currently rank1: Qian Zhang(Beijing Samsung Telecom R&D Center), 0.6410@top1, 0.9065@top5
• homepage: http://places.csail.mit.edu/user/leaderboard.php

Blogs

What is the class of this image ? - Discover the current state of the art in objects classification

• intro: “Discover the current state of the art in objects classification.”
• intro: MNIST, CIFAR-10, CIFAR-100, STL-10, SVHN, ILSVRC2012 task 1
• blog: http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library

http://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/

The Effect of Resolution on Deep Neural Network Image Classification Accuracy

https://medium.com/the-downlinq/the-effect-of-resolution-on-deep-neural-network-image-classification-accuracy-d1338e2782c5#.em5rk991r