Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/angelvillar96/FaceEmoji

Deep Residual Learning for Image Recognition

https://github.com/anguyen8/sam

Going Deeper with Convolutions

https://github.com/anh-nn01/Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/anhnktp/yolov3

YOLOv3: An Incremental Improvement

https://github.com/anibali/dsnt-pose2d

Deep Residual Learning for Image Recognition

https://github.com/aniket-patra-1998/FLOWER_RECOGNITION

Deep Residual Learning for Image Recognition

https://github.com/aniket03/pirl_pytorch

Self-Supervised Learning of Pretext-Invariant Representations

https://github.com/aniketmaurya/ssd-tf2-tfds

SSD: Single Shot MultiBox Detector

https://github.com/animikhaich/single-object-background-subtractor

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/aninair1905/Abnormal-Event-Detection

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/anirudh-chakravarthy/CASENet

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/anish9/GCPAnet-tensorflow2.2

Global Context-Aware Progressive Aggregation Network for Salient Object Detection

https://github.com/anishreddy3/Autonomous-driving-application-car-detection

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

https://github.com/anjandeepsahni/face_classification

Deep Residual Learning for Image Recognition

https://github.com/anjandeepsahni/face_recognition

Deep Residual Learning for Image Recognition

https://github.com/ankit-ai/GAN_breast_mammography_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ankit-vaghela30/Google-landmark-prediction

Deep Residual Learning for Image Recognition

https://github.com/ankitAMD/Darkflow-object-detection

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

https://github.com/ankitshah009/CarCrash_forecasting_and_detection

CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis

https://github.com/ankitsinghsuraj/mot20

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

https://github.com/ankush-me/SynthText

Synthetic Data for Text Localisation in Natural Images

https://github.com/anminhhung/FPT_Hackathon

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/annabellachen/newDarknet

YOLO9000: Better, Faster, Stronger

https://github.com/annosubmission/GRC-Cache

Cached Transformers: Improving Transformers with Differentiable Memory Cache

https://github.com/annstrange/breast-cancer-cnn

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

https://github.com/anonymous-koala-supporter/adversarial-deep-metric-learning

Going Deeper with Convolutions

https://github.com/anonymous1125/patnet_dataset

Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

https://github.com/anonymousjack/hijacking

Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking

https://github.com/anookeen/yolo

YOLO9000: Better, Faster, Stronger

https://github.com/anoushkrit/Knowledge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/anshky/HR-NET

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/anshulpaigwar/Frustum-Pointpillars

https://github.com/anthonyhuo/sam-dam-for-compositional-reasoning

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/antonilo/unsupervised_detection

Unsupervised Moving Object Detection via Contextual Information Separation

https://github.com/antonoeschmidt/darknet

YOLOv3: An Incremental Improvement

https://github.com/anxingle/UNet-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

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