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/nanzhaogang/contrib/tree/master/application/dropblock-a-regularization-method-for-convolutional-networks

DropBlock: A regularization method for convolutional networks

https://github.com/nanzhaogang/contrib/tree/master/application/group-normalization

Group Normalization

https://github.com/naoya0731/deepmosaic

SSD: Single Shot MultiBox Detector

https://github.com/naoya0731/deepmozaik_sample

SSD: Single Shot MultiBox Detector

https://github.com/napolar/simple_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/narumiruna/efficientnet-pytorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/nasa-impact/hls-foundation-os

Masked Autoencoders Are Scalable Vision Learners

https://github.com/nasir6/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/nasirtrekker/personcounter

SSD: Single Shot MultiBox Detector

https://github.com/nataliele/waldo

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/natanielruiz/android-yolo

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

https://github.com/nathanlem1/igae-net

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/native2019/hello

YOLO9000: Better, Faster, Stronger

https://github.com/natureyoo/continualtta_objectdetection

What, How, and When Should Object Detectors Update in Continually Changing Test Domains?

https://github.com/nautilus261/tf-faster-rcnn

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

https://github.com/naver-ai/BlendNeRF

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/naver-ai/pflayer

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/naver/tldr

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/navidhasanzadeh/UNet-Semantic-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/naviocean/CenterNet

Objects as Points

https://github.com/nayanr/instance_segmentation

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/nbouteldja/kidneystaintranslation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ndb796/pytorch-adversarial-training-cifar

Deep Residual Learning for Image Recognition

https://github.com/necla-ml/Deformable-ConvNets-py3

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/neeraj-j/MixNet

MixConv: Mixed Depthwise Convolutional Kernels

https://github.com/neilctwu/FCOS-pytorch_Simplified

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/nemonameless/PaddleDetection_YOLOSeries

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/nemonameless/PaddleDetection_YOLOX

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/nemonameless/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/nemonameless/mmkp

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/neptune-ai/open-solution-mapping-challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/nerminsamet/houghnet-vid

HoughNet: Integrating near and long-range evidence for visual detection

https://github.com/neshitov/UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/neuleaf/MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/neung678/anti_bird

YOLOv3: An Incremental Improvement

https://github.com/neuqgz/modify-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.