Papers with Code 2020 : A Year in Review

Ross Taylor
Dec 30, 2020 · 3 min read

Papers with Code indexes various machine learning artifacts — papers, code, results — to facilitate discovery and comparison. Using this data we can get a sense of what the ML community found useful and interesting this year. Below we summarize the top trending papers, libraries and benchmarks for 2020 on Papers with Code.

Top Trending Papers of 2020

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EfficientDet by Tan et al was the most viewed paper on Papers with Code for 2020
  1. EfficientDet: Scalable and Efficient Object Detection — Tan et al https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object
  2. Fixing the train-test resolution discrepancy — Touvron et al https://paperswithcode.com/paper/fixing-the-train-test-resolution-discrepancy-2
  3. ResNeSt: Split-Attention Networks — Zhang et al https://paperswithcode.com/paper/resnest-split-attention-networks
  4. Big Transfer (BiT) — Kolesnikov et al https://paperswithcode.com/paper/large-scale-learning-of-general-visual
  5. Object-Contextual Representations for Semantic Segmentation — Yuan et al https://paperswithcode.com/paper/object-contextual-representations-for
  6. Self-training with Noisy Student improves ImageNet classification — Xie et al https://paperswithcode.com/paper/self-training-with-noisy-student-improves
  7. YOLOv4: Optimal Speed and Accuracy of Object Detection — Bochkovskiy et al https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
  8. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale — Dosovitskiy et al https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1
  9. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer — Raffel et al https://paperswithcode.com/paper/exploring-the-limits-of-transfer-learning
  10. Hierarchical Multi-Scale Attention for Semantic Segmentation — Tao et al https://paperswithcode.com/paper/hierarchical-multi-scale-attention-for

Top Trending Libraries of 2020

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🤗 Transformers was the most viewed library on Papers with Code for 2020
  1. Transformers — Hugging Face — https://github.com/huggingface/transformers
  2. PyTorch Image Models — Ross Wightman — https://github.com/rwightman/pytorch-image-models
  3. Detectron2 — FAIR — https://github.com/facebookresearch/detectron2
  4. InsightFace — DeepInsight — https://github.com/deepinsight/insightface
  5. Imgclsmob — osmr — https://github.com/osmr/imgclsmob
  6. DarkNet — pjreddie — https://github.com/pjreddie/darknet
  7. PyTorchGAN — Erik Linder-Norén — https://github.com/eriklindernoren/PyTorch-GAN
  8. MMDetection — OpenMMLab — https://github.com/open-mmlab/mmdetection
  9. FairSeq — PyTorch — https://github.com/pytorch/fairseq
  10. Gluon CV — DMLC — https://github.com/dmlc/gluon-cv

Top Trending Benchmarks of 2020

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ImageNet was the most viewed benchmark on Papers with Code for 2020
  1. ImageNet — Image Classification — https://paperswithcode.com/sota/image-classification-on-imagenet
  2. COCO — Object Detection / Instance Segmentation — https://paperswithcode.com/sota/object-detection-on-coco
  3. Cityscapes — Semantic Segmentation — https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes
  4. CIFAR-10 — Image Classification — https://paperswithcode.com/sota/image-classification-on-cifar-10
  5. CIFAR-100 — Image Classification — https://paperswithcode.com/sota/image-classification-on-cifar-100
  6. PASCAL VOC 2012 — Semantic Segmentation — https://paperswithcode.com/sota/semantic-segmentation-on-pascal-voc-2012
  7. MPII Human Pose — Pose Estimation — https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose
  8. Market-1501 — Person Re-Identification — https://paperswithcode.com/sota/person-re-identification-on-market-1501
  9. MNIST — Image Classification — https://paperswithcode.com/sota/image-classification-on-mnist
  10. Human 3.6M — Human Pose Estimation -https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose

Congratulations to all the authors and developers for making an impact in the ML community this year!

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Thanks to Elvis Saravia for helping put this piece together

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