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/anthonyhuo/sam-dam-for-compositional-reasoning

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/antonilo/unsupervised_detection

Unsupervised Moving Object Detection via Contextual Information Separation

https://github.com/antonoeschmidt/darknet

YOLOv3: An Incremental Improvement

https://github.com/anxingle/UNet-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/anzhao0503/group-normalization.pytorch

Group Normalization

https://github.com/aoru45/BasketNet

SSD: Single Shot MultiBox Detector

https://github.com/aoru45/LFFD-Pytorch

SSD: Single Shot MultiBox Detector

https://github.com/aotumanbiu/awesome-object-detection

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/apacha/OMR-Datasets

DoReMi: First glance at a universal OMR dataset

https://github.com/apacha/musicobjectdetector-tf

YOLO9000: Better, Faster, Stronger

https://github.com/aparecidovieira/Road_extraction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/apchenstu/SLN-Amodal

Semantic Amodal Segmentation

https://github.com/apolloauto/apollo-model-yolox

YOLOX: Exceeding YOLO Series in 2021

https://github.com/apple/ml-cubifyanything

Cubify Anything: Scaling Indoor 3D Object Detection

https://github.com/apple/ml-cvpr2019-swd

Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

https://github.com/apple/ml-mobileclip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/applied-machine-learning-lab/awesome-multimodal-recommender-systems

MM-Rec: Multimodal News Recommendation

https://github.com/aqbewtra/Multi-Class-Aerial-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/arahosu/musicslots

Deep Residual Learning for Image Recognition

https://github.com/arahusky/Tensorflow-Segmentation

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

https://github.com/arashjavan/deeplio

PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

https://github.com/arbaza/3D-object-detection-KITTI

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

https://github.com/archit31uniyal/DC-Gnet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/areberoto/darknet-thesis

YOLOv3: An Incremental Improvement

https://github.com/areffarhadi/mamba-asr

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

https://github.com/arghadeep25/Face-Detection

Max-Margin Object Detection

https://github.com/ariG23498/mae-scalable-vision-learners

Masked Autoencoders Are Scalable Vision Learners

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/arifromadhan19/rcnn-pneumonia

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/aritzLizoain/Image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/arjunakula/neurips2021

CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions

https://github.com/armaank/archlectures

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/armin-azh/3DefficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/arobey1/advbench

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/arobey1/mbrdl

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/aronfothi/mask_cluster_rcnn

MMDetection: Open MMLab Detection Toolbox and Benchmark

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