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/DengPingFan/DAVSOD

https://github.com/DengPingFan/SINet

https://github.com/DentanJeremie/adversarialTransferts

Task-generalizable Adversarial Attack based on Perceptual Metric

https://github.com/DequanWang/tent

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

https://github.com/DerrickGXD/unsupervised-keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DerryHub/ByteTrack-attack

Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers

https://github.com/DetectionTeamUCAS/FCOS_GluonCV

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/DetectionTeamUCAS/FPN_Tensorflow

Feature Pyramid Networks for Object Detection

https://github.com/DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RRPN_Faster_RCNN_Tensorflow

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation

Focal Loss for Dense Object Detection

https://github.com/DevBruce/PyTorch-ISBI2012-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevBruce/Segmentation-UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevBruce/YOLO-TF2

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

https://github.com/DevBruce/YOLOv1-TF2

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

https://github.com/DevBruce/YOLOv3-TF2

YOLOv3: An Incremental Improvement

https://github.com/DevPranjal/reproduction-review-kd

Distilling Knowledge via Knowledge Review

https://github.com/DhruvMakwana/Global-Wheat-Detection

Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods

https://github.com/Di-Is/stylegan2-ada-pytorch

Efficient Attention: Attention with Linear Complexities

https://github.com/DiMarzioRock7/Aerial-Semantic-Segmentation-Drone

Pyramid Scene Parsing Network

https://github.com/DiMarzioRock7/PSPNet

Pyramid Scene Parsing Network

https://github.com/DianaSHV/lemniscate_edit

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/DiegoArcelli/De-Stylization-Network

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/Discover304/MMDetection3D-Eloss

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/DivJAth/DeepLearning5922

Mask R-CNN

https://github.com/Dmitrsl/Tools

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/DmitryRyumin/ICCV-2023-Papers

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/DoTung-bkhn/multiclass-segmentation

Pyramid Scene Parsing Network

https://github.com/Dodant/ANPR-with-Yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DominickZhang/Distillation-Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/DonCorle0ne/damage_detection

YOLO9000: Better, Faster, Stronger

https://github.com/Donvink/Car_Detection_for_Autonomous_Driving

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

https://github.com/DoranLyong/SPANet-official

Spectral-Adaptive Modulation Networks for Visual Perception

https://github.com/Doyosae/Deep-Complex-Networks

On Complex Valued Convolutional Neural Networks

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