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/sevakon/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/seyrankhademi/ResNet_CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/sforsyth6/cifar_lem

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/sforsyth6/coco_lem

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/sforsyth6/lemniscate-original

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/sgrvinod/a-pytorch-tutorial-to-object-detection

Scalable Object Detection using Deep Neural Networks

https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution

Deep Residual Learning for Image Recognition

https://github.com/sh4175515/PointTransformer

Point Transformer

https://github.com/shaikkhasim/-TensorFlow.js-Transfer-Learning-Image-Classifier

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/shallowtoil/DROL

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/shamitlal/CoCoNets

CoCoNets: Continuous Contrastive 3D Scene Representations

https://github.com/shanglianlm0525/CvPytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/shanky1947/YOLOv3-Darknet-Custom-Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/shao-chi/ImageCaption

Deep Residual Learning for Image Recognition

https://github.com/shaojiawei07/tocom-tem

Multiview Detection with Feature Perspective Transformation

https://github.com/sharifelguindi/DeepLab

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/shashigharti/secure-privateai-scholarship-challenge

Focal Loss for Dense Object Detection

https://github.com/shawnyuen/DataAugmentationPaperCollection

Random Erasing Data Augmentation

https://github.com/shayandaneshvar/gui-element-detection

GUI Element Detection Using SOTA YOLO Deep Learning Models

https://github.com/shayeree96/Adversial-Attacks-on-densely-fused-point-clouds-for-6D-Pose-Estimation

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/shehroze37/Augmented-Deep-Reinforcement-Learning-for-3D-environments

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

https://github.com/shellhue/detectron2-ResNeSt

ResNeSt: Split-Attention Networks

https://github.com/shellredia/snake-swin-octa

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/shendb2022/htd-mamba

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/shenshutao/image_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/shenyunhang/WS-JDS

https://github.com/shenyunhang/caffe-fwsl

SSD: Single Shot MultiBox Detector

https://github.com/sherdencooper/tricks-in-deeplearning

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/shermanhung/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/shibuiwilliam/Keras_Autoencoder

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/shicai/MobileNet-Caffe

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/shichaoy/cuboid_detect

CubeSLAM: Monocular 3D Object SLAM

https://github.com/shichaoy/matlab_cuboid_detect

CubeSLAM: Monocular 3D Object SLAM

https://github.com/shier1/CycleMLP-paddle

CycleMLP: A MLP-like Architecture for Dense Prediction

https://github.com/shighghyujie/infrared_patch_attack

Physically Adversarial Infrared Patches with Learnable Shapes and Locations

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