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/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

Pyramid Scene Parsing Network

https://github.com/PaParaZz1/TemporalShiftModule

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PASSL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/PaddlePaddle/PLSC

ConvMAE: Masked Convolution Meets Masked Autoencoders

https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/swin

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/PaddlePaddle/PLSC/tree/master/task/ssl/cae

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dino

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det

Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/smalldet

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vitdet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/PaddlePaddle/PaddleFL

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/PaddleMIX

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

https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

https://github.com/PaddlePaddle/PaddleOCR

Synthetic Data for Text Localisation in Natural Images

https://github.com/PaddlePaddle/PaddleSeg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PaddleYOLO

You Only Look One-level Feature

https://github.com/PaddlePaddle/VIMER

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/PaddlePaddle/models

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/PaiHsuehChung/DockerYOLOV3

YOLOv3: An Incremental Improvement

https://github.com/Pangoraw/GeoSSL

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/PaperCodeReview/BarlowTwins-TF

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/PaperCodeReview/PixPro-TF

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

https://github.com/Papirapi/Few_shot-learning-for-Object-Detection

Few-shot Object Detection via Feature Reweighting

https://github.com/Param-Raval/ipro

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/Param-Raval/shadow-sight

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

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