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/jajajajaja121/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/jakc4103/DFQ

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/jakc4103/piecewise-quantization

Post-Training Piecewise Linear Quantization for Deep Neural Networks

https://github.com/jakeret/tf_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jakeret/unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jaketae/mlp-mixer

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/jaleedkhan/zsrr-sgg

https://github.com/jalotra/Queue-Detection

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

https://github.com/jalotra/Queue-Detection-

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

https://github.com/james77777778/keras-image-models

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/jameswang287/Car-Detection

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/jamiljami/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jaminfong/fna

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

https://github.com/jankrepl/mildlyoverfitted

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/jarrodanderson/openpose-demo

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

https://github.com/jaryp/confidencebranchnetwok

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/jasjeetIM/Mask-RCNN

Mask R-CNN

https://github.com/jason-hu7/PyTorch-YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/jason-su/UCGNet

CornerNet-Lite: Efficient Keypoint Based Object Detection

https://github.com/jason90330/EdgeFinal

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/javiergarciamolina/selfie-background-removal

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jayin301/2021AutonomousACCar

YOLOv3: An Incremental Improvement

https://github.com/jbardelli/Petrographic-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jd730/OICR-pytorch

Multiple Instance Detection Network with Online Instance Classifier Refinement

https://github.com/jday96314/Kuzushiji

Deep Residual Learning for Image Recognition

https://github.com/jediofgever/PointNet_Custom_Object_Detection

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

https://github.com/jeffshih/autoTrain

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

https://github.com/jeffwiroj/robust_tutorial

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/jellevankerk/Team-Challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jellywangjie/yolov3

YOLOv3: An Incremental Improvement

https://github.com/jeong-tae/SNIP-filter-level-pytorch

SNIP: Single-shot Network Pruning based on Connection Sensitivity

https://github.com/jeongjin0/detector-cleanse

BadDet: Backdoor Attacks on Object Detection

https://github.com/jercas/TextBoxes_plusplus_tf

TextBoxes++: A Single-Shot Oriented Scene Text Detector

https://github.com/jerichooconnell/tf_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jeromepan/Image-motion-delbur-with-a-smart-phone-application

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/jeromerony/survey_wsl_histology/blob/6d98928a1da2c73d84a791a11a6f18acd647d9cd/mil/poolings/wildcat.py

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