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/hhuaibo/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/hidekazu300/test

SSD: Single Shot MultiBox Detector

https://github.com/highwaywu/tianchi-fft2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hikapok/tf-senet

Squeeze-and-Excitation Networks

https://github.com/hila-chefer/robustvit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/hirohiro23/Darknet

YOLO9000: Better, Faster, Stronger

https://github.com/hisiter97/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/hitachi-rd-cv/MILA

MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection

https://github.com/hitchingsh/ml-examples

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/hitukensinn/quiz-w9-code

Fully Convolutional Networks for Semantic Segmentation

https://github.com/hj963434688/Faster_rcnn

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

https://github.com/hkbu-vscomputing/2022_mm_dmae-mocap

Masked Autoencoders Are Scalable Vision Learners

https://github.com/hku-medai/mgca

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

https://github.com/hkust-aerial-robotics/sg-reg

FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

https://github.com/hkust-aerial-robotics/slabim

FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

https://github.com/hl-hanlin/smkd

ImageNet Large Scale Visual Recognition Challenge

https://github.com/hll110/DIoU-darknet-master

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/hlld/cross-iteration-batch_normalization

Cross-Iteration Batch Normalization

https://github.com/hm7455/Anti-collision_Semantic-segmentation_

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/hmichaeli/alias_free_convnets

A ConvNet for the 2020s

https://github.com/hnmspirit/malaria_centernet

Objects as Points

https://github.com/hoaitrinh459/RobotNhatBongTennis

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hoangcuongbk80/VoteGrasp

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/hoanguyen81190/Synopticon-CenterNet

Objects as Points

https://github.com/holdfire/CLS

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/holdfire/FAS

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/hollyprince/ObjectsDetection-CornerNet-Lite

CornerNet-Lite: Efficient Keypoint Based Object Detection

https://github.com/holyseven/PSPNet-TF-Reproduce

Pyramid Scene Parsing Network

https://github.com/holytrick/AlexeyAB_darknet

YOLO9000: Better, Faster, Stronger

https://github.com/hongfz16/DS-Net

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

https://github.com/hongkunsun/paratranscnn

Deep Residual Learning for Image Recognition

https://github.com/hongru-seu/PointPilliar

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/hongyi-zhang/Fixup

Deep Residual Learning for Image Recognition

https://github.com/hope-yao/robust_attention_cifar

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/horrible-dong/teamdetr

DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

https://github.com/horse007666/ResNet

Deep Residual Learning for Image Recognition

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