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/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/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/Lopezurrutia/DSB_2018

Mask R-CNN

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

https://github.com/Lornatang/PyTorch-yolov3

YOLOv3: An Incremental Improvement

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

Perceptual Extreme Super Resolution Network with Receptive Field Block

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

Deep Residual Learning for Image Recognition

https://github.com/Lornatang/YOLOv3-PyTorch

YOLOv3: An Incremental Improvement

https://github.com/Lornatang/YOLOv4-PyTorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Louis24/Segmentation

MegDet: A Large Mini-Batch Object Detector

https://github.com/LuanAdemi/VisualGo

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LucaHermes/graph-unet-traffic-prediction

U-Net: Convolutional Networks for Biomedical Image Segmentation

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