Papers with Code 中收集了各種機器學習的內(nèi)容：論文，代碼，結(jié)果，方便發(fā)現(xiàn)和比較。通過這些數(shù)據(jù)，我們可以了解ML社區(qū)中，今年哪些東西最有意思。下面我們總結(jié)了2020年最熱門的帶代碼的論文、代碼庫和benchmark。

2020頂流論文

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

2020頂流代碼庫

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

2020頂流Benchmarks

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