Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/C677/Machine-learning

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/CAMMA-public/ai4surgery

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/CAPTAIN-WHU/DOTA_devkit

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

https://github.com/CASIA-IVA-Lab/ISP-reID

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/CMU-Perceptual-Computing-Lab/openpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/CRIGIM/darknet

YOLOv3: An Incremental Improvement

https://github.com/CS-GangXu/TMNet

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/CSAILVision/semantic-segmentation-pytorch

Pyramid Scene Parsing Network

https://github.com/CSAILVision/unifiedparsing

Acquisition of Localization Confidence for Accurate Object Detection

https://github.com/CSSFA123/yolov4-Road

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/CUAI-CAU/Computer_vision_team_1

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

https://github.com/CV-deeplearning/caffe

SSD: Single Shot MultiBox Detector

https://github.com/CV51GO/GFLv2_Megengine

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

https://github.com/CV51GO/HarDNet_Megengine

HarDNet: A Low Memory Traffic Network

https://github.com/CV51GO/OTA_Megengine

OTA: Optimal Transport Assignment for Object Detection

https://github.com/CV51GO/PAA_Megengine

Probabilistic Anchor Assignment with IoU Prediction for Object Detection

https://github.com/CVUsers/Smart-Retail-By-Efficientdet

Feature Pyramid Networks for Object Detection

https://github.com/CVxTz/face_age_gender

Deep Residual Learning for Image Recognition

https://github.com/CWanli/myencoding

Context Encoding for Semantic Segmentation

https://github.com/CZQ0707/mindspore

YOLOv3: An Incremental Improvement

https://github.com/Cadden/paddle-adver

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/CahideSara/deneme

YOLO9000: Better, Faster, Stronger

https://github.com/CaiYingFeng/darknet_changed

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/CallHoganZhang/unet-medical_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Calylyli/Mindsporehrnet

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Camebush/real-time-yolov4-object-detection

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/CandiceD17/Bottleneck-Transformers-for-Visual-Recognition

Bottleneck Transformers for Visual Recognition

https://github.com/CanshangD/ResNet_tensorflow2.0

Deep Residual Learning for Image Recognition

https://github.com/Capino512/pytorch-rotation-decoupled-detector

https://github.com/Capsar/DL-GenISP

GenISP: Neural ISP for Low-Light Machine Cognition

https://github.com/CarkusL/CenterPoint

Center-based 3D Object Detection and Tracking

https://github.com/Carlisle-Liu/OCENet

Confidence-Aware Learning for Camouflaged Object Detection

https://github.com/CarloLepelaars/filter_response_normalization_keras

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/CarryHJR/Nested-UNet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/CarstenIsert/DeepBurn

Mask R-CNN

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