Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/OFRIN/Tensorflow_RetinaFace

Focal Loss for Dense Object Detection

https://github.com/OFRIN/Tensorflow_YOLOv1

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

https://github.com/OIPLab-DUT/ATSA

https://github.com/OIPLab-DUT/CVPR2020-A2dele

https://github.com/OIPLab-DUT/CVPR_SSF-RGBD

https://github.com/OML-Team/open-metric-learning

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/OceanPang/Libra_R-CNN

Libra R-CNN: Towards Balanced Learning for Object Detection

https://github.com/Ohyeon5/MismatchPenaltySegmentation

SSD: Single Shot MultiBox Detector

https://github.com/Ohyeon5/MultiscaleSegmentation

SSD: Single Shot MultiBox Detector

https://github.com/Oichii/DeepPulse-pytorch

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

https://github.com/Okery/PyTorch-Simple-MaskRCNN

Mask R-CNN

https://github.com/OlafenwaMoses/Action-Net

Deep Residual Learning for Image Recognition

https://github.com/OlafenwaMoses/FireNET

YOLOv3: An Incremental Improvement

https://github.com/OlafenwaMoses/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/OlafenwaMoses/Traffic-Net

Deep Residual Learning for Image Recognition

https://github.com/Olayemiy/Image-Classification-With-Resnet

Deep Residual Learning for Image Recognition

https://github.com/OlgaFomin/Speech-enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/OliverJBoom/Histopathologic-Cancer-Detection

Squeeze-and-Excitation Networks

https://github.com/Omar-Awad/SCNN_GPU2

SBNet: Sparse Blocks Network for Fast Inference

https://github.com/OniroAI/Instance-segmentation-with-MobileNetV3

Searching for MobileNetV3

https://github.com/OpenCv30/Yolov3

YOLOv3: An Incremental Improvement

https://github.com/OpenFirework/pytorch_solov2

SOLOv2: Dynamic and Fast Instance Segmentation

https://github.com/OpenSpaceAI/DEST3D

State Space Model Meets Transformer: A New Paradigm for 3D Object Detection

https://github.com/OscarPellicer/prostate_lesion_detection

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/Osdel/ssnets

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Oussamayousre/Yoga_pose_recognizer

Feature Pyramid Networks for Object Detection

https://github.com/Owais-Ansari/Unet3plus

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/OwlTing/AI_basketball_games_video_editor

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/P2333/Max-Mahalanobis-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/PB17151764/2020UM-Summer-Research

Deep Retinal Image Understanding

https://github.com/PINTO0309/PINTO_model_zoo/tree/main/326_YOLOPv2

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

https://github.com/POSTECH-CVLab/FastPointTransformer

Point Transformer

https://github.com/POSTECH-CVLab/point-transformer

Point Transformer

https://github.com/PPPrior/i3d-pytorch

Deep Residual Learning for Image Recognition

https://github.com/PPPrior/u2net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/PRBonn/bonnet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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