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/baneitixiaomai/joint_sod_cod

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

https://github.com/banus/umf_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/baodi23/hourglass-facekeypoints-detection

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

https://github.com/baoshengyu/deep-high-resolution-net.pytorch

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/bareblackfoot/IDNet

Learning Instance-Aware Object Detection Using Determinantal Point Processes

https://github.com/bareblackfoot/faster-rcnn.selection

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

https://github.com/bareblackfoot/lddp-tf-faster-rcnn

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

https://github.com/barmayo/spatial_attention

Deep Residual Learning for Image Recognition

https://github.com/basaltzhang/maskrcnn-benchmark

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/basiclab/DA-OD-MEAA-PyTorch

https://github.com/baskargroup/Arboretum

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/baskargroup/biotrove

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bazilas/matconvnet-deepReg

Robust Optimization for Deep Regression

https://github.com/bborja/mods_evaluation

MODS -- A USV-oriented object detection and obstacle segmentation benchmark

https://github.com/bclwan/MRI_Brain_Segmentation

Deep Residual Learning for Image Recognition

https://github.com/bcmi/TopNet-Object-Placement

Learning to Generate Synthetic Data via Compositing

https://github.com/bcmi/madisnet-inharmonious-region-localization

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/beapc18/AffordanceNet

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

https://github.com/beassssry/U

Fast R-CNN

https://github.com/becauseofAI/DetNet-Keras

DetNet: A Backbone network for Object Detection

https://github.com/befallenStar/molecularAtt

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/beltransen/lidar_bev

BirdNet: a 3D Object Detection Framework from LiDAR information

https://github.com/ben-davidson-6/fixup

Deep Residual Learning for Image Recognition

https://github.com/ben0110/Frustum_pointnet_2D

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/ben0110/Radar-Pointnet-PARA

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/ben0110/frustum-pointnets_RSC_RADAR_fil_PC_batch_para

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/benihime91/pytorch_retinanet

Feature Pyramid Networks for Object Detection

https://github.com/benjaminrwilson/inequity-release

Predictive Inequity in Object Detection

https://github.com/benjamintli/darknet-gun-detector

YOLO9000: Better, Faster, Stronger

https://github.com/benjamintli/training-results-azure

YOLO9000: Better, Faster, Stronger

https://github.com/bennyqp/artificial-inspiration

Going Deeper with Convolutions

https://github.com/bentrevett/pytorch-image-classification

Deep Residual Learning for Image Recognition

https://github.com/benywon/ChiQA

End-to-End Object Detection with Transformers

https://github.com/berenslab/retinal-vessel-segmentation-benchmark

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/beresandras/contrastive-classification-keras

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/berkandemirel/zero-shot-detection

Zero-Shot Object Detection by Hybrid Region Embedding

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