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/sdroh1027/DiffusionVID

https://github.com/sdu2011/darknet_alexyab

YOLO9000: Better, Faster, Stronger

https://github.com/seanmor5/tf_lambda

LambdaNetworks: Modeling Long-Range Interactions Without Attention

https://github.com/seansoleyman/cifar10-resnet

Deep Residual Learning for Image Recognition

https://github.com/sebastian-lapuschkin/lrp_toolbox

Towards Best Practice in Explaining Neural Network Decisions with LRP

https://github.com/sebastiani/pytorch-attention-augmented-convolution

Attention Augmented Convolutional Networks

https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN

Deep Residual Learning for Image Recognition

https://github.com/sebsquire/FlowersRecognition_keras

Deep Residual Learning for Image Recognition

https://github.com/secretlyvogon/IndRNNTF

Squeeze-and-Excitation Networks

https://github.com/secretlyvogon/Neural-Network-Implementations

Squeeze-and-Excitation Networks

https://github.com/securade/hub

A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans

https://github.com/see--/keras-centernet

Objects as Points

https://github.com/seermer/TensorFlow2-EfficientNetV2

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/seishinkikuchi/test

Deep Residual Learning for Image Recognition

https://github.com/sek788432/Intel-Scene-Image-Classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/sek788432/Microsoft-Cats-and-Dogs-Image-Classification

Random Erasing Data Augmentation

https://github.com/semskurto/APTOS

Group Normalization

https://github.com/sense-x/co-detr

DETRs with Collaborative Hybrid Assignments Training

https://github.com/sense-x/uniformer

UniFormer: Unifying Convolution and Self-attention for Visual Recognition

https://github.com/seominseok0429/Learning-Loss-for-Active-Learning-Pytorch

Learning Loss for Active Learning

https://github.com/seominseok0429/inception-I3D-NON-LOCAL

Non-local Neural Networks

https://github.com/seominseok0429/pytorch-warmup-cosine-lr

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/seominseok0429/vidoe_tracking-Reidentification-Action_recognition_Demo

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

https://github.com/seongilp/DFE604-2020F-FinalProject

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/sepuhopar/ReportDD-darknet

YOLOv3: An Incremental Improvement

https://github.com/serengil/deepface

SSD: Single Shot MultiBox Detector

https://github.com/serengil/retinaface

Fast R-CNN

https://github.com/sergiosonline/transfercv

YOLOv3: An Incremental Improvement

https://github.com/sermanet/OverFeat

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

https://github.com/serre-lab/gala_tpu

Deep Residual Learning for Image Recognition

https://github.com/setharram/facenet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/seujung/WeightStandardization_gluon

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

https://github.com/seunghwan1228/CNN_EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/seungjunlee96/U-Net_Lung-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/seungwonoh5/Universal_Autonomous_Detection

Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations

https://github.com/sevakon/efficientdet

EfficientDet: Scalable and Efficient Object Detection

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