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/vlievin/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vobecant/yolov3_WannaSeeU

YOLOv3: An Incremental Improvement

https://github.com/vohoaiviet/OverFeat-DeepNet

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

https://github.com/vohoaiviet/retrieval-2016-deepvision

Faster R-CNN Features for Instance Search

https://github.com/void-in-the-matrix/nuclei-segmentation-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/voidrank/Geo-CNN

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

https://github.com/voidrank/SaccadeNet

Objects as Points

https://github.com/volgachen/Pix2Seq_Pytorch

Pix2seq: A Language Modeling Framework for Object Detection

https://github.com/vov-net/VoVNet-Detectron

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

https://github.com/vov-net/VoVNet-FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/vqdang/hover_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vqdang/xy_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vturrisi/solo-learn

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/vuongtrannguyenkhoi/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/vvasilo/yolov3_pytorch_ros

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

https://github.com/w-sugar/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/w-sugar/prtr

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/w5688414/Keras-MobileNetV2-Image-classification

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/wL1u/CenterNet-VisDrone

Objects as Points

https://github.com/waltersimoncini/fungivision

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/wang-chen/correlation_flow

Kernel Cross-Correlator

https://github.com/wang-zidu/3ddfa-v3

Searching for MobileNetV3

https://github.com/wangdongdut/dut-anti-uav

Fully-Convolutional Siamese Networks for Object Tracking

https://github.com/wangermeng2021/EfficientDet-tensorflow2

EfficientDet: Scalable and Efficient Object Detection

https://github.com/wangermeng2021/FastClassification

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/wangermeng2021/PVT-tensorflow2

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2

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

https://github.com/wangermeng2021/ScaledYOLOv4-tensorflow2

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/wangguangyuan/ClusterContrast

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/wangkingkingking/ssd_mathocr

SSD: Single Shot MultiBox Detector

https://github.com/wanglaotou/centernet_ncnn

Objects as Points

https://github.com/wangrui1996/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/wangsr126/mae-lite

Masked Autoencoders Are Scalable Vision Learners

https://github.com/wangtuo0820/maskrcnn-benchmark-expansion

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

https://github.com/wangvation/torch-mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/wangxianhao/Improved-YOLOv3-for-UAV

Focal Loss for Dense Object Detection

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