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/code-implementation1/Code9/tree/main/PointNet2

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

https://github.com/code-implementation1/Code9/tree/main/RetinaFace_ResNet50

Region Proposal by Guided Anchoring

https://github.com/code-implementation1/Code9/tree/main/SSD

SSD: Single Shot MultiBox Detector

https://github.com/code-implementation1/Code9/tree/main/ShuffleNet

Slimmable Neural Networks

https://github.com/code-implementation1/Code9/tree/main/SwinTransformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/code-implementation1/Code9/tree/main/VIT

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

https://github.com/code-implementation1/Code9/tree/main/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code9/tree/main/YOLOv3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code9/tree/main/YOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/code-implementation1/Code9/tree/main/YOLOv5

YOLO5Face: Why Reinventing a Face Detector

https://github.com/cokowpublic/Nuclei-Segmentation

Mask R-CNN

https://github.com/coldsummerday/mmdetection-zhou

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/collectionslab/Omniscribe

Mask R-CNN

https://github.com/collectionslab/annotations-computervision

Mask R-CNN

https://github.com/collectionslab/book-annotation-classification

Mask R-CNN

https://github.com/colorfulxd/WK10_FCN

Fully Convolutional Networks for Semantic Segmentation

https://github.com/computationalpathologygroup/hvit

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/conceptofmind/Crossformer-flax

CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention

https://github.com/conceptofmind/PiT-flax

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/conceptofmind/Region-ViT-flax

RegionViT: Regional-to-Local Attention for Vision Transformers

https://github.com/condiff-dataset/ConDiff

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/conradry/copy-paste-aug

Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

https://github.com/conradry/pixpro-with-weights

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

https://github.com/contactnipun/Computer-Vision

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/coolbay/VSGN

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

https://github.com/cooldawnant/infochartqa

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics

https://github.com/coperception/coperception

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

https://github.com/coperception/double-m-quantification

Uncertainty Quantification of Collaborative Detection for Self-Driving

https://github.com/coreqode/HardNet

HarDNet: A Low Memory Traffic Network

https://github.com/cornell-zhang/allo-pldi24-artifact

Deep Residual Learning for Image Recognition

https://github.com/cosmic-cortex/pytorch-UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/cpn/ShuffledVideoSorter

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn

Mask R-CNN

https://github.com/cs-giung/course-dl-TP

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cshizhe/maskrcnn_benchmark

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/csm-kr/Retinanet_pytorch

Focal Loss for Dense Object Detection

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