Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

124

보존된 평가 기록

977

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mtcld/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/mtli/sAP

Towards Streaming Perception

https://github.com/muddge/yolov3-person-only

YOLOv3: An Incremental Improvement

https://github.com/muditchaudhary/RepPoints-x-Libra-R-CNN-x-Transformer-self-attention

RepPoints: Point Set Representation for Object Detection

https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/mulinmeng/Shuffle-Transformer

Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

https://github.com/multimodallearning/pytorch-mask-rcnn

Mask R-CNN

https://github.com/munachisonwadike/YOLOV3

YOLOv3: An Incremental Improvement

https://github.com/munjalbharti/Knowledge-Distillation-for-End-to-End-Person-Search

Knowledge Distillation for End-to-End Person Search

https://github.com/muraliadithya/vdp

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/muramasa8191/DeepLearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/murdockhou/Yet-Another-EfficientDet-Pytorch-Convert-ONNX-TVM

EfficientDet: Scalable and Efficient Object Detection

https://github.com/murufeng/awesome_lightweight_networks

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

https://github.com/musifahamran/FYP

Deep Residual Learning for Image Recognition

https://github.com/mutichung/yolov3_pt

YOLOv3: An Incremental Improvement

https://github.com/muyiguangda/pytorch-yolov3

YOLOv3: An Incremental Improvement

https://github.com/muyistarsky/MaskRCNN

Mask R-CNN

https://github.com/muzairkhattak/multimodal-prompt-learning

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/mv-lab/kuzushiji-recognition

CenterNet: Keypoint Triplets for Object Detection

https://github.com/mvenouziou/Project-Attention-Is-What-You-Get

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mvpzhangqiu/SOLOV2

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/mvxxx/mimuw-hats

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/mx-mark/videotransformer-pytorch

Masked Autoencoders Are Scalable Vision Learners

https://github.com/myfavouritekk/tpn

Object Detection in Videos with Tubelet Proposal Networks

https://github.com/mzeromiko/vmamba

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/nabarunbaruaAIML/ATCC_Yolov5

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/nachiket273/VisTrans

Bottleneck Transformers for Visual Recognition

https://github.com/nachiket273/loss-tryout

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/nachiket273/pytorch_resnet_rs

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/nadermx/backgroundremover

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/nadinenijssen/Github_5AUA0_Project_G12T1

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/nahidalam/open_clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/naiyer-abbas/Detection-Classification-and-Semantic_Segmentation-of-apples

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

https://github.com/najlaeLemrabet/FacialKeypointsDetection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/namakemono/kaggle-alaska2-image-steganalysis

AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights

https://github.com/namdvt/Focal-loss-pytorch-implementation

Focal Loss for Dense Object Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.