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/mohammadreyaz/openposered

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

https://github.com/mohammadreyaz/pose

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

https://github.com/mohanliu/HRnet-semantic-segmentation-exp

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mohitktanwr/Deep-Stem-ResNeSt-ISPRS

ResNeSt: Split-Attention Networks

https://github.com/mohitktanwr/ResNeSt_Inverse

ResNeSt: Split-Attention Networks

https://github.com/molyswu/knowledge-graph-embeding

YOLOv3: An Incremental Improvement

https://github.com/mon95/centerpoint-maps

Center-based 3D Object Detection and Tracking

https://github.com/monk-ai/maskrcnn

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

https://github.com/morganmcg1/stanford-cars

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/morrisxu-driving/video-music_cross-modal_retrival

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/moskomule/senet.pytorch

Squeeze-and-Excitation Networks

https://github.com/motlabs/dont-be-turtle

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/motokimura/PyTorch_Gaussian_YOLOv3

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

https://github.com/mr-bulb/DL_basic_github

Deep Residual Learning for Image Recognition

https://github.com/mrgiovanni/dissertation

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

https://github.com/mrvturan96/Brain-Tumor-Detection-and-Segmentation-using-Deep-Learning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mrzzy/np-dl-assign-1

Deep Residual Learning for Image Recognition

https://github.com/mseg-dataset/mseg-semantic

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mshane911/NRP

A Self-supervised Approach for Adversarial Robustness

https://github.com/mshn2000/PointFusion

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

https://github.com/msminhas93/deeplabv3finetuning

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/mssung94/intel-image-classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/mstfakts/building-detection-maskrcnn

Mask R-CNN

https://github.com/msuhail1997/Fast-RCNN-Pytorch

Fast R-CNN

https://github.com/msuhail1997/Faster-RCNN-Pytorch_Object_Detection

Fast R-CNN

https://github.com/msuhail1997/YOLO-Pytorch

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

https://github.com/msuhail1997/YOLO-Pytorch-Object_Detection

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

https://github.com/msuhail1997/YOLOv1-Pytorch

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

https://github.com/mszpc/MSG-beihang

MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens

https://github.com/mszpc/nanodet

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

https://github.com/mszpc/resnet18

Deep Residual Learning for Image Recognition

https://github.com/mszpc/yolox

YOLOX: Exceeding YOLO Series in 2021

https://github.com/mtancak/PyTorch-UNet-Brain-Cancer-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mtancak1/PyTorch-UNet-BraTS20

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mtancak1/PyTorch-UNet-Brain-Cancer-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mtcld/maskrcnn_benchmark_pytorch

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

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