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/YunanWu2168/Stock_Prediction_Project
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
https://github.com/Yuxiang1995/ICDAR2021_MFD
https://github.com/Yuzi0123/MindSpore_Yolov5
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZC119/Handwritten-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ZFT-CQU/DSDL
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ZFTurbo/segmentation_models_3D
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ZH-Lee/Tensorflow-Mobilenet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/ZJCV/KnowledgeReview
https://github.com/ZJCV/ZCls
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/ZJZAC/Poison-Ink
https://github.com/ZQPei/deep_sort_pytorch
https://github.com/ZTao-z/multiflow-resnet-ssd
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/ZUCCBBQ/yoloV3-
https://github.com/ZVK/Talking-Heads
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/ZVK/talking_heads
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/ZXdatascience/TextDetectorAndroid
https://github.com/ZZR8066/mmdetection-GaofenCompetition
https://github.com/Zachdr1/CNNs
https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Zahra2351373/Image-Processing-Final-Project
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Zakobian/CT_framework_
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Zanderzt/TensorRT_Mask_RCNN
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/ZeeTsing/Carvana_challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Zehaos/MobileNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Zekromaegis/Neural-Style-Transfer
https://github.com/Zeng555/EMV-YOLO
Getting to Know Low-light Images with The Exclusively Dark Dataset
https://github.com/Zengyi-Qin/TLNet
Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
https://github.com/Zero-nnkn/ml-utils/blob/main/cv/pose/openpose_predictor.py
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ZhZiKai/VisDrone_FCOS
https://github.com/Zhachory1/MusicNST
https://github.com/Zhang-Jing-Xuan/MaskRCNN
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.