Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision

Going Deeper with Convolutions

https://github.com/STomoya/ResNeSt

ResNeSt: Split-Attention Networks

https://github.com/SUYEgit/Surgery-Robot-Detection-Segmentation

Mask R-CNN

https://github.com/Saadbutt321/Conversion-of-Pakistan-Sign-Language-into-Text-and-Speech-using-OpenPose-and-Machine-Learning

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/SafiyaJan/Attacking-Neural-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/SahinTiryaki/Brain-tumor-segmentation-Vgg19UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Sai-Vamsi-Ogety/object_detection

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/SaifeddineBarkia/Multi-view-radar-semantic-segmentation-for-scene-understanding-in-autonomous-driving

Multi-View Radar Semantic Segmentation

https://github.com/Sakib1263/1DResNet-Builder-KERAS

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/1DResNet-KERAS

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/Inception-InceptionResNet-1D-2D-Tensorflow-Keras

Going Deeper with Convolutions

https://github.com/Sakib1263/Inception-Model-Builder-Tensorflow-Keras

Going Deeper with Convolutions

https://github.com/Sakib1263/MobileNet-1D-2D-Tensorflow-Keras

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Sakib1263/ResNet-Model-Builder-KERAS

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras

Deep Residual Learning for Image Recognition

https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS

Deep Residual Learning for Image Recognition

https://github.com/Saloni1Parekh609/MIDAS

End-to-End Object Detection with Transformers

https://github.com/SalvadorAlbarran/TFG2020

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Sangkwun/Resnet

Deep Residual Learning for Image Recognition

https://github.com/SanketAgrawal/Catagorical-Focal-Loss

Focal Loss for Dense Object Detection

https://github.com/SarthakGarg13/OTOMYCOSIS

Deep Residual Learning for Image Recognition

https://github.com/SarthakGarg13/Severstal-Steel-Defect-Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SarthakYadav/audax

A ConvNet for the 2020s

https://github.com/SatyamGaba/lt_od

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/SdahlSean/FilterResponseNormalizationTensorflow

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

https://github.com/SeHwanJoo/mmdetection_vinbig

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/SebasGarcia08/sku110-dense-object-mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Seioch/darknet_v4_deploy

YOLOv3: An Incremental Improvement

https://github.com/SeniorDesignF20/AmazonUnderstandingProductImages

Spot the Difference by Object Detection

https://github.com/Sense-X/TSD

Revisiting the Sibling Head in Object Detector

https://github.com/Sentient07/HeadHunter-T

Tracking Pedestrian Heads in Dense Crowd

https://github.com/SeonbeomKim/TensorFlow-pix2pix

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SeonghoBaek/CPC

Data-Efficient Image Recognition with Contrastive Predictive Coding

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