Papers with Code 아카이브

Object Detection

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하위 태스크

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평가표

124

보존된 평가 기록

977

코드

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

https://github.com/sanjeewk/ppnet

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/sanoyo/dl-model

Deep Residual Learning for Image Recognition

https://github.com/santhisenan/car-detection-using-yolov2

YOLO9000: Better, Faster, Stronger

https://github.com/sara-nl/SURF-deeplab

EfficientDet: Scalable and Efficient Object Detection

https://github.com/sara-nl/SURF-segmentation

EfficientDet: Scalable and Efficient Object Detection

https://github.com/saransh317/YOLOv3-Easy-Implementation

YOLOv3: An Incremental Improvement

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

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

https://github.com/saswat0/Thundernet-Object-Detection

ThunderNet: Towards Real-time Generic Object Detection

https://github.com/sathishkumark27/mmdetection-icevision

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/sathvikyesprabhu/brats-dl

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/satyaborg/pose-estimation-detection

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/saumya-jetley/pp_ICVSS15_TacklingBkgndDifferently

Fast R-CNN

https://github.com/saunack/MobileNetv2-SSD

SSD: Single Shot MultiBox Detector

https://github.com/saurabh241930/yolo_v2_tensorflow

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

https://github.com/sauravmishra1710/Inception---Going-Deeper-with-Convolutions/tree/main/InceptionV1

Going Deeper with Convolutions

https://github.com/sauravmishra1710/UNet-Plus-Plus---Brain-Tumor-Segmentation

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

https://github.com/savageyusuff/MobilePose-Pi

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/sayakpaul/Adaptive-Gradient-Clipping

Deep Residual Learning for Image Recognition

https://github.com/sayakpaul/Barlow-Twins-TF

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/sayakpaul/ConvNeXt-TF

A ConvNet for the 2020s

https://github.com/sayakpaul/swin-transformers-tf

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/sbadredd/semantic-pascal-part

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

https://github.com/sberbank-ai/ru-clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/sbetageri/MaskRCNN

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/sbunian/VINS

VINS: Visual Search for Mobile User Interface Design

https://github.com/scenarri/s2m-tea

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/schenock/u2net-generic

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

https://github.com/scheshmi/BottleneckTransformers

Bottleneck Transformers for Visual Recognition

https://github.com/schopfej/pytorch_unet_jvanvugt

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/scienXX/roomcamera2

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/sciosci/graph_check

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/scolocke/Traffic_Sign_ID_GTSRB_GTSDB

Mask R-CNN

https://github.com/scrambleegg7/ssd_prescription

SSD: Single Shot MultiBox Detector

https://github.com/sd2001/UNets

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sdkchris/Exercise-Sheet-1

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/sdll/hrnet-pose-estimation

Deep High-Resolution Representation Learning for Human Pose Estimation

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