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/Discover304/MMDetection3D-Eloss

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/DivJAth/DeepLearning5922

Mask R-CNN

https://github.com/Dmitrsl/Tools

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/DmitryRyumin/ICCV-2023-Papers

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/DoTung-bkhn/multiclass-segmentation

Pyramid Scene Parsing Network

https://github.com/Dodant/ANPR-with-Yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DominickZhang/Distillation-Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/DonCorle0ne/damage_detection

YOLO9000: Better, Faster, Stronger

https://github.com/DonkeyShot21/UNO

A Unified Objective for Novel Class Discovery

https://github.com/Donvink/Car_Detection_for_Autonomous_Driving

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/DoranLyong/SPANet-official

Spectral-Adaptive Modulation Networks for Visual Perception

https://github.com/Doyosae/Deep-Complex-Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Deep_Complex_Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Stand-Alone-Self-Attention

Stand-Alone Self-Attention in Vision Models

https://github.com/DrMMZ/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/Dragoniss/mindspore-phase2-Deep-Fourier-Upsampling/blob/main/Fourier-Upsampling.py

Deep Fourier Up-Sampling

https://github.com/DuaneNielsen/keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DucQuan12/Classification-with-activation-mish

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/Duplums/bhb10k-dl-benchmark

Deep Residual Learning for Image Recognition

https://github.com/Dycollapsar/Attention-Based-for-Medicalimaging

Squeeze-and-Excitation Networks

https://github.com/Dylan-get/APCNet

https://github.com/E-DEEP/PapersReview

Zero-Shot Instance Segmentation

https://github.com/EMI-Group/FaPN

Feature Pyramid Networks for Object Detection

https://github.com/EMI-Group/TransferLearning-Tasks

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

https://github.com/EMalagoli92/GCViT-TensorFlow

Global Context Vision Transformers

https://github.com/EMalagoli92/VAN-Classification-TensorFlow

Visual Attention Network

https://github.com/EMsnap/RobotSorting

YOLO9000: Better, Faster, Stronger

https://github.com/ENSTA-U2IS/MUAD-Dataset

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EPFL-VILAB/3DCommonCorruptions

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EPFL-VILAB/XDEnsembles

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EbadSyed/spadRGBD

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

https://github.com/Ededu1984/car_detection_forza_horizon

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation

Deep Residual Learning for Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation

Deep Residual Learning for Image Recognition

https://github.com/Edmonton-School-of-AI/ml5-Simple-Image-Classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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