Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/suraj-maniyar/Object-Tracking-SSD300

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/sushmaakoju/yolo

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/suvasis/birdnet2cs231n

TensorMask: A Foundation for Dense Object Segmentation

https://github.com/suyash16999/Social-Distance-Monitoring

Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques

https://github.com/suyash16999/Social-Distancing

Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques

https://github.com/svjack/Detector-in-Detector

Detector-in-Detector: Multi-Level Analysis for Human-Parts

https://github.com/swasun/VQ-VAE-images

Deep Residual Learning for Image Recognition

https://github.com/swathi-469/Teresa-Bot

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/swaubhik/social-distance-detector

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/swetak20/Deep_into_CNNs

Going Deeper with Convolutions

https://github.com/switch23/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/switchablenorms/SwitchNorm_Segmentation

Pyramid Scene Parsing Network

https://github.com/syiin/ann_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/syiin/human_protein_atlas

Squeeze-and-Excitation Networks

https://github.com/syiin/resnet_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/symoon94/YOLO-keras

YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

https://github.com/syscv/ovtrack

Quasi-Dense Similarity Learning for Multiple Object Tracking

https://github.com/szacho/augmix-tf

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

https://github.com/szagoruyko/imagine-nn

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/szegedai/robust-segmentation-evaluation

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

https://github.com/szq0214/CMC_with_Image_Mixture

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/t-hanya/kuzushiji-recognition

Objects as Points

https://github.com/t0930198/backup

Deep Residual Learning for Image Recognition

https://github.com/t0nyliang/EEGMobile

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/taashi-s/M2Det_keras

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/taco-group/stamp

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

https://github.com/tade0726/yolov3_ui_error

YOLOv3: An Incremental Improvement

https://github.com/taegoobot/aerial-object-detector

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

https://github.com/taeokimeng/object-detection-yolo

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/taha7ussein007/Papers_Implementation/tree/main/Paper_Implementation_From_Scratch/UNet_FromScratch_Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/tahmid0007/DETR_FineTune

End-to-End Object Detection with Transformers

https://github.com/tailhq/dynaml

Going Deeper with Convolutions

https://github.com/taited/clip-score

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/taki0112/AdamP-Tensorflow

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

https://github.com/taki0112/Group_Normalization-Tensorflow

Group Normalization

https://github.com/takooctopus/CenterNet-Tako

CenterNet: Keypoint Triplets for Object Detection

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