Papers with Code 아카이브

16k

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/LeeDongYeun/keras-m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/LeeGitaek/Kaggle_Solving

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/LeeJunHyun/Image_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Leg-end/Detection

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

https://github.com/Legoons/Melanoma_classification

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Legoons/Whale_Classification

Squeeze-and-Excitation Networks

https://github.com/LeoXuZH/CervicalCancerDetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LeonSun0101/CD-SD

Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

https://github.com/Leonardo-Blanger/detr_tensorflow

End-to-End Object Detection with Transformers

https://github.com/Leozhibin/PReNet-master

Progressive Image Deraining Networks: A Better and Simpler Baseline

https://github.com/Li-Chongyi/cmMS-ECCV20

RGB-D Salient Object Detection with Cross-Modality Modulation and Selection

https://github.com/Li-ai-cell/Interpretation_DETR

End-to-End Object Detection with Transformers

https://github.com/LiYingwei/ShapeTextureDebiasedTraining

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

https://github.com/LiangSiyuan21/Adversarial-Attacks-for-Image-and-Video-Object-Detection/tree/master/img_attack_with_attention

Transferable Adversarial Attacks for Image and Video Object Detection

https://github.com/LibrarristShalinward/while_true_involute-Cream_of_the_Crop

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

https://github.com/LilyHu/image_segmentation_chromosomes

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LinHuiTeng/TrashClassifier

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/LinRiver/YOLOv3-on-LISA-Traffic-Sign-Detection-with-darknet

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/LinZhuoChen/pointnet2_multi_gpu

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/Linby1103/Darknet-Python-Interface

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Linxuxin/mmdetection-mine

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Little-BigMan/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LiuShenLan/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Liuyubao/transfer-learning

Going Deeper with Convolutions

https://github.com/Lkruitwagen/remote-sensing-solar-pv

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Lmath11/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/Lmonster/darknet

YOLOv3: An Incremental Improvement

https://github.com/LokeZhou/mydetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LongguangWang/ArbSR

Learning A Single Network for Scale-Arbitrary Super-Resolution

https://github.com/LongguangWang/Scale-Arbitrary-SR

Learning A Single Network for Scale-Arbitrary Super-Resolution

https://github.com/Lopezurrutia/DSB_2018

Mask R-CNN

https://github.com/LorisNanni/Improving-existing-segmentators-performance-with-zero-shot-segmentators

https://github.com/Lornatang/CSNLN-PyTorch

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

https://github.com/Lornatang/DRRN-PyTorch

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/Lornatang/ESPCN-PyTorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/Lornatang/ESRGAN-PyTorch

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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