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://isrc.iscas.ac.cn/gitlab/research/locount-dataset.git

공식 코드

Rethinking Object Detection in Retail Stores

https://worksheets.codalab.org/worksheets/0xbcd424d2bf544c4786efcc0063759b1a

공식 코드

Going Deeper with Convolutions

https://zenodo.org/record/12611978

공식 코드

MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception

https://dagshub.com/Bharat-mtr/DeepFlux

DeepFlux for Skeletons in the Wild

https://dagshub.com/Bharat-mtr/EGNet

https://dagshub.com/Bharat-mtr/yolov1-tf2

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

https://dagshub.com/arnavr.neo/VT-LPR

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

https://dagshub.com/arnavr.neo/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation

Zero-Shot Instance Segmentation

https://gitee.com/dakerqinqi/wslnet

A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

https://gitee.com/gai-shaoyan/mind3d

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://gitee.com/ke-01/tnet_mindspore

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

https://github.com/0bserver07/Keras-SegNet-Basic

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

https://github.com/0jason000/GoogleNet

Going Deeper with Convolutions

https://github.com/0jason000/MobileNet_V2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/0jason000/MobilleNetV3

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/0jason000/ShuffleNetV1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/0jason000/convnext

A ConvNet for the 2020s

https://github.com/0jason000/mae_vit

Masked Autoencoders Are Scalable Vision Learners

https://github.com/0xPr0xy/CoreML

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/0xPr0xy/MobileNet-CoreML

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/0xc4f3/dfuc2022_snippets

DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection

https://github.com/0xzayd/Wnet-cGAN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/1044197988/TF.Keras-Commonly-used-models

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/111qqz/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/13274086/Tianchi2020

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/13952522076/mmdet0

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/13952522076/mmdet1

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/1512159/tf-faster-rcnn-medico

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

https://github.com/15526837635/CenterPoint

Center-based 3D Object Detection and Tracking

https://github.com/15534081591/FairMOT

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/163GitHub/AI

Pyramid Scene Parsing Network

https://github.com/16xccheng/keras-unet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/1991yuyang/YOLOV1-PYTORCH

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

https://github.com/1hb6s7t/MindSpore-Uncertainty_Calibration_Object_Detection-

A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection

https://github.com/2006pmach/awesome-few-shot-object-detection

Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

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