Papers with Code 아카이브

Object Detection

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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/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/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

https://github.com/Cassie94/LSTD

LSTD: A Low-Shot Transfer Detector for Object Detection

https://github.com/CatOneTwo/WSDDN-PyTorch

Weakly Supervised Deep Detection Networks

https://github.com/CeLaMbDa/DSOD_keras

DSOD: Learning Deeply Supervised Object Detectors from Scratch

https://github.com/Cedric-Perauer/Deformable_Detr_PIL

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/CellProfiling/HPA-Special-Prize

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/CellSMB/EM-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Chamchis/AICCTV

YOLO9000: Better, Faster, Stronger

https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2

Focal Loss for Dense Object Detection

https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models

Deep Residual Learning for Image Recognition

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