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/sidSingla/RegNetx-YOLOv3

YOLOv3: An Incremental Improvement

https://github.com/siddhanthaldar/PyTorch_Object_Detection

MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects

https://github.com/siddhantkhandelwal/deblur-gan

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/siddinc/monocular_depth_estimation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sidify/areal_image_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sidify/resnet_focal_loss

Deep Residual Learning for Image Recognition

https://github.com/signatrix/efficientdet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/sigtot/unet-auto

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/silist/mmdet_dev

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/simongrest/kaggle-freesound-audio-tagging-2019

Squeeze-and-Excitation Networks

https://github.com/simonlevine/11785-project

Focal Loss for Dense Object Detection

https://github.com/simonlevine/clinical-longformer

Focal Loss for Dense Object Detection

https://github.com/simplew2011/YOLO_ZOO

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/sinandeger/RedeemTheBoar

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sincerass/mvlpt

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/singhaman092/rembg

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/sithu31296/image-classification

MetaFormer Is Actually What You Need for Vision

https://github.com/sithu31296/pose-estimation

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/sithu31296/semantic-segmentation

HarDNet: A Low Memory Traffic Network

https://github.com/sithu31296/simple-object-tracking

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/sivaole/Face_Detection

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/siyuanliii/masa

DETRs with Collaborative Hybrid Assignments Training

https://github.com/siyuhuang/TOD

Learning Loss for Active Learning

https://github.com/sjosias/Tsetse-Flies

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

https://github.com/sk1123344/U-2-NET-and-U-NET

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/skandermoalla/bet-reproduction

Behavior Transformers: Cloning $k$ modes with one stone

https://github.com/skbadhsm/darknet2

YOLO9000: Better, Faster, Stronger

https://github.com/skhadem/3D-BoundingBox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/skiteskopes/FID_CULLING

Training Domain Specific Models for Energy-Efficient Object Detection

https://github.com/skiteskopes/datasetCulling

Training Domain Specific Models for Energy-Efficient Object Detection

https://github.com/skrish13/SKU110K-benchmark

Precise Detection in Densely Packed Scenes

https://github.com/skrish13/SKU110K-evaluation

Precise Detection in Densely Packed Scenes

https://github.com/skyhehe123/voxelnet-pytorch

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/smallcorgi/3D-Deepbox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/smallcorgi/Faster-RCNN_TF

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

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