Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

124

보존된 평가 기록

977

코드

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

https://github.com/nuannuanhcc/ps_reppoint

RepPoints: Point Set Representation for Object Detection

https://github.com/nuoaier/BaiduStar2020-18-Competition-Solution

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

https://github.com/nurulxakmar/darknet

YOLOv3: An Incremental Improvement

https://github.com/nutintin/Robotic_SP2019

SSD: Single Shot MultiBox Detector

https://github.com/nv-tlabs/planning-centric-metrics

The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes

https://github.com/nvidia-ai-iot/lidar_ai_solution

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/nvlabs/parametricbev

Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View

https://github.com/nweakly/MSDSProject-II

YOLO9000: Better, Faster, Stronger

https://github.com/nyak10/mwc

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/o-evgeny/MRCNN_DeepFashion2

Mask R-CNN

https://github.com/oakdata/benchmark

Wanderlust: Online Continual Object Detection in the Real World

https://github.com/oakleighw/BRATS-Pretext-Tasks-for-Self-supervised-Deep-Learning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/oandrienko/fast-semantic-segmentation

Pyramid Scene Parsing Network

https://github.com/oaqoe-DWQ/Yolov5_DeepSort_Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/oarriaga/paz

Perception for Autonomous Systems (PAZ)

https://github.com/object-detection-algorithm/RPN

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

https://github.com/object-detection-algorithm/SSD

SSD: Single Shot MultiBox Detector

https://github.com/oci-ai/oci-dreamer

Going Deeper with Convolutions

https://github.com/oeway/pytorch-deform-conv

Deformable Convolutional Networks

https://github.com/ogatarina-sq/secyolov7

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/ohshyuk5/Memory-based-Exchangeable-Model

Learning Permutation Invariant Representations using Memory Networks

https://github.com/oist/DenseObjectAnnotation

Towards dense object tracking in a 2D honeybee hive

https://github.com/oist/DenseObjectDetection

Towards dense object tracking in a 2D honeybee hive

https://github.com/okboseong/darknet

YOLOv3: An Incremental Improvement

https://github.com/oki5656/Estimating_Distance

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/olahosa/adl_cancer_detection

Detecting Cancer Metastases on Gigapixel Pathology Images

https://github.com/oliverquintana/UnetSegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/oliverz17/Unet_Keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/oljikeboost/PlayerTracking

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

https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm

Deep Residual Learning for Image Recognition

https://github.com/om-ai-lab/OmAgent

How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection

https://github.com/om-ai-lab/ZoomEye

How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection

https://github.com/omni-us/squeezedet-keras

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/omrijsharon/torchlex

On Complex Valued Convolutional Neural Networks

https://github.com/oneflow-inc/libai

Masked Autoencoders Are Scalable Vision Learners

https://github.com/oneformer3d/oneformer3d

OneFormer3D: One Transformer for Unified Point Cloud Segmentation

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