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Object

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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/viethungluu/OCRetina

Focal Loss for Dense Object Detection

https://github.com/vietlinhtspt/NewFasterRCNN

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

https://github.com/vigansub/StructureAwareShapeTemplates

Parsing Geometry Using Structure-Aware Shape Templates

https://github.com/vik-goel/MOREL

Unsupervised Video Object Segmentation for Deep Reinforcement Learning

https://github.com/vikrant7/mobile-vod-bottleneck-lstm

Mobile Video Object Detection with Temporally-Aware Feature Maps

https://github.com/vimalabs/VIMA

https://github.com/vincentzhang/faster-rcnn-fcn

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

https://github.com/vinliao/object-detection-python

YOLO9000: Better, Faster, Stronger

https://github.com/viplix3/BoTSORT-cpp

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

https://github.com/visaVita/mmaction2

Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

https://github.com/vishal3477/proactive-object-detection

PrObeD: Proactive Object Detection Wrapper

https://github.com/vishwesh5/Paper-a-day

OCNet: Object Context Network for Scene Parsing

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

SSD: Single Shot MultiBox Detector

https://github.com/vision-agh/pointpillars-backbone

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/vision-agh/pointpillars_backbone

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/visionxlab/lrs-vqa

FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

https://github.com/viskjha/Wrong-way-vehicle-detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/vita-epfl/rock-pytorch

SSD: Single Shot MultiBox Detector

https://github.com/vitae-transformer/qformer

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/vivek20dadhich/deep-learning-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/vlcekl/n2n-tomo

Microsoft COCO: Common Objects in Context

https://github.com/vlievin/Unet

Group Normalization

https://github.com/vnbot2/object-locator

Locating Objects Without Bounding Boxes

https://github.com/vnoves/aectech2019-sketchto3d-backend

Hierarchical Surface Prediction for 3D Object Reconstruction

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-FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/vpaliwal1/Image-Classification-using-CNN

Striving for Simplicity: The All Convolutional Net

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/prtr

End-to-End Object Detection with Transformers

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

Objects as Points

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/Scaled-YOLOv4-tensorflow2

YOLOv4: Optimal Speed and Accuracy of Object Detection

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