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/AmeerHamza111/yolo-darknet

YOLOv3: An Incremental Improvement

https://github.com/AmirDavoodi/Hand-Gestures-Human-Robot-Interaction

SSD: Single Shot MultiBox Detector

https://github.com/AmirNiaraki/Visual-Exploration

Visual Exploration and Energy-aware Path Planning via Reinforcement Learning

https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Amisha-here/Image-Classifier

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AmitNativ1984/yolov3

YOLOv3: An Incremental Improvement

https://github.com/AmrElsersy/PointPainting

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/Ander02/FaceRecognition

SSD: Single Shot MultiBox Detector

https://github.com/Anderies/stochastic-constant-pruninglayers

Deep Residual Learning for Image Recognition

https://github.com/AndreiMoraru123/Hydra

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AndresPMD/Clip_CMR

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Andrew-Qibin/DSS

Deeply supervised salient object detection with short connections

https://github.com/AndreydeAguiarSalvi/yolo_compression

YOLOv3: An Incremental Improvement

https://github.com/AndrzejMiskow/FER-with-Attention-and-Objective-Activation-Functions

Squeeze-and-Excitation Networks

https://github.com/Andy-Dang12/Activation-Functions

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/AngusG/bn-advex-zhang-fixup

Deep Residual Learning for Image Recognition

https://github.com/Angzz/fpn-gluon-cv

Feature Pyramid Networks for Object Detection

https://github.com/Anirudh0707/Roads-and-Building-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AnirudhAchal/Human-Parsing

Pyramid Scene Parsing Network

https://github.com/Anmol6/capshun

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/AnselmC/bamot

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/AntixK/PyTorch-Model-Compare

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/AnudeepKonda/MIMII_anamoly_detection

ResNeSt: Split-Attention Networks

https://github.com/Aprilkaka/nonlocal_mindspore

Non-local Neural Networks

https://github.com/ArashJavan/PointSeg

PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

https://github.com/Arcady1/Doodle-Recognition-Web

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ArcherFMY/AFNet

https://github.com/ArchieAlexArkhipov/Cycle-CenterNet

Parsing Table Structures in the Wild

https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification

Going Deeper with Convolutions

https://github.com/ArivCR7/Melanoma_Classifier

Squeeze-and-Excitation Networks

https://github.com/ArkaJU/U-Net-Satellite

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Arno3165229/CornerNet_Traffic_Light

CornerNet-Lite: Efficient Keypoint Based Object Detection

https://github.com/ArsenalCheng/Meta-Adapter

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

https://github.com/ArtLabss/tennis-tracking

YOLOv3: An Incremental Improvement

https://github.com/Arthur-Shi/py-faster-rcnn

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

https://github.com/Artsplendr/Medical-Images-Liver-Segmentation-NIfTI

U-Net: Convolutional Networks for Biomedical Image Segmentation

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