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/thisisi3/Paddle-GHM

Gradient Harmonized Single-stage Detector

https://github.com/thisisi3/Paddle-YOLOF

You Only Look One-level Feature

https://github.com/thodorostsao/4-leg-robot-neural-network

YOLO9000: Better, Faster, Stronger

https://github.com/thomasbrandon/mish-cuda

Mish: A Self Regularized Non-Monotonic Activation Function

https://github.com/thomashopkins32/PGDAdversarialLearning

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/thomasslloyd/FitSpatial

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/three0-s/MAE-keras

Masked Autoencoders Are Scalable Vision Learners

https://github.com/thtrieu/darkflow

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

https://github.com/thudm/visualglm-6b

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/thughost2/Padam

Deep Residual Learning for Image Recognition

https://github.com/thuml/Transfer-Learning-Library

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

https://github.com/thuml/Transfer-Learning-Library/tree/dev-tllib/examples/domain_adaptation/object_detection

Decoupled Adaptation for Cross-Domain Object Detection

https://github.com/thusiyuan/holistic_scene_parsing

Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

https://github.com/thuxyz19/test

3D Human Pose Estimation with Spatial and Temporal Transformers

https://github.com/tiagoCuervo/JapaNet

Deep Residual Learning for Image Recognition

https://github.com/tianchiVideoSeg/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tiancity-NJU/da-faster-rcnn-PyTorch

Domain Adaptive Faster R-CNN for Object Detection in the Wild

https://github.com/tianhai123/deform-conv

Deformable Convolutional Networks

https://github.com/tianhai123/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tianhai123/tf-faster-rcnn

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

https://github.com/tianhai123/yolov3

YOLO9000: Better, Faster, Stronger

https://github.com/tianrun-chen/sam-adapter-pytorch

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

https://github.com/tianweiy/CenterPoint-KITTI

Center-based 3D Object Detection and Tracking

https://github.com/tianyaoZhang/DeeCamp

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/tianzheng4/Distributionally-Adversarial-Attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/tiendatnguyen-vision/UNet-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/tiev-tongji/quadric_slam

CubeSLAM: Monocular 3D Object SLAM

https://github.com/tiger154/captcha-solver-custom

Going Deeper with Convolutions

https://github.com/tigerofmurder/tf-faster-rcnn

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

https://github.com/timmeinhardt/trackformer

Make One-Shot Video Object Segmentation Efficient Again

https://github.com/tinymilky/RSANet

Non-local Neural Networks

https://github.com/titu1994/Keras-Group-Normalization

Group Normalization

https://github.com/titu1994/MobileNetworks

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/titu1994/keras-attention-augmented-convs

Attention Augmented Convolutional Networks

https://github.com/titu1994/keras-efficientnets

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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