Papers with Code 아카이브
2D Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SDL-GuoZonghao/BeyondBoundingBox
https://github.com/SEU-SRTP-GROUP/frustum-pointnet-original
https://github.com/SH-Tan/RangeRcnn-backbone
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation
https://github.com/SHAHBAZSIDDIQI/DataScience
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SHI-Labs/Decoupled-Classification-Refinement
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
https://github.com/SHU-FLYMAN/Yolov1-TensorFlow
https://github.com/SK124/Semantic-Segmentaion-Using-UNets
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
https://github.com/SSRSGJYD/NeuralTexture
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/STomoya/ResNeSt
https://github.com/SUYEgit/Surgery-Robot-Detection-Segmentation
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/Saaiheswar/Neural-Style-Transfer
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
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/Inception-InceptionResNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/Inception-Model-Builder-Tensorflow-Keras
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
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Saloni1Parekh609/MIDAS
https://github.com/SalvadorAlbarran/TFG2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/SambhawDrag/XLNet.jl
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
https://github.com/SamuelBroughton/StarGAN-Voice-Conversion
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
https://github.com/SamuelBroughton/StarGAN-Voice-Conversion-2
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.