Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

20

보존된 평가 기록

57

코드

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

https://github.com/earth-insights/ClassTrans

Aligning and Prompting Everything All at Once for Universal Visual Perception

https://github.com/easonxiao-888/mindomni

GrootVL: Tree Topology is All You Need in State Space Model

https://github.com/easternRainy/MSDS-DL-Final-YOLO-Implement

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

https://github.com/easyadin/Object-Detection-YOLOv4

YOLOv3: An Incremental Improvement

https://github.com/eavise-kul/lightnet

YOLO9000: Better, Faster, Stronger

https://github.com/eddiebarry/SingleShotDetector

SSD: Single Shot MultiBox Detector

https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking

Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

https://github.com/edgarrt/custom_darknet

YOLO9000: Better, Faster, Stronger

https://github.com/edisonlxy/detectiontool

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/eduard626/interaction-tensor-affordances

What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection

https://github.com/edwardclem/deepscribe

Focal Loss for Dense Object Detection

https://github.com/edwardning/Pytorch-EfficientDet-ModelArts

EfficientDet: Scalable and Efficient Object Detection

https://github.com/edwardyang12/Sign-Language-Detection

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

https://github.com/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eetkim/physatt

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/eftPavel/Hack-Aubg-2018

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/egeonat/MS-CornerNet

CornerNet: Detecting Objects as Paired Keypoints

https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection

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

https://github.com/eify/open_clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ekzhang/fastseg

Searching for MobileNetV3

https://github.com/elasticdeeplearning/edl

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/eldadp100/Towards-Deep-Learning-Models-Resistant-to-Adversarial-Attacks-Implementation

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eldadp100/cnn_course_final

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/elonashatri/pitch_mask_rcnn

Mask R-CNN

https://github.com/elras/desmokenet

An All-in-One Network for Dehazing and Beyond

https://github.com/emannix/automating-the-assessment-of-biofouling

ComFe: Interpretable Image Classifiers With Foundation Models, Transformers and Component Features

https://github.com/embracesource-cv-com/FCHD

FCHD: Fast and accurate head detection in crowded scenes

https://github.com/emilianavt/OpenSeeFace

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/eminorhan/resnext-wsl

Exploring the Limits of Weakly Supervised Pretraining

https://github.com/emportent/Bonobo_Chimp

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/engelnico/point-transformer

Point Transformer

https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/ensta-u2is/torch-uncertainty

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/enzomuschik/distilfnd

Deep Residual Learning for Image Recognition

https://github.com/eogussla12/Shufflenet_CIFAR10_Pytorch

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/epoc88/PFLD_68pts_Pytorch_2020

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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