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2D Classification

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코드

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

https://github.com/DannisZgggg/AnoGAN-WGAN-pytorch

https://github.com/Daonancai/unet_muti_class

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DarkGeekMS/Pytorch-YOLOv3-Implementation

YOLOv3: An Incremental Improvement

https://github.com/DarkGeekMS/Semantic_Segmentation_Models_Keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/DarkGeekMS/artistic-style-transfer-using-texture-synthesis

Style-Transfer via Texture-Synthesis

https://github.com/DarshanDeshpande/jax-models

Squeeze-and-Excitation Networks

https://github.com/DarshanDeshpande/research-paper-implementations/tree/main/tensorflow/stytr2

StyTr$^2$: Image Style Transfer with Transformers

https://github.com/Data-drone/attention_augmented_cnn

Attention Augmented Convolutional Networks

https://github.com/DataCanvasIO/Hypernets

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

https://github.com/DataGenReserarchTeam/expo_markers

Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data

https://github.com/DataXujing/Cascade_RCNN_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/DataXujing/DIoU_YOLO_V3

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/DataXujing/EfficientDet_pytorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/DataXujing/ExtremeNet-Pytorch

Bottom-up Object Detection by Grouping Extreme and Center Points

https://github.com/DataXujing/TensorRT-DETR

End-to-End Object Detection with Transformers

https://github.com/DataXujing/YOLOR-

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/DataXujing/YOLOX-

YOLOX: Exceeding YOLO Series in 2021

https://github.com/DavHoffmann/Multi-humanDataGeneration

Learning to Train with Synthetic Humans

https://github.com/DavianYang/yolo.ai

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

https://github.com/David-zaiwang/114_rgbd_seg

Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation

https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DavidLandup0/deepvision

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/Davidnh8/artAI

A Neural Algorithm of Artistic Style

https://github.com/Dawars/SqueezeNet-tf

A Neural Algorithm of Artistic Style

https://github.com/Dayan-Guan/DA-VSN

Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

https://github.com/DeLightCMU/MAL

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeLightCMU/MAL-inference

Multiple Anchor Learning for Visual Object Detection

https://github.com/DeNA/Chainer_Mask_R-CNN

Mask R-CNN

https://github.com/DeNA/PyTorch_YOLOv3

YOLOv3: An Incremental Improvement

https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

https://github.com/DeadNud1e/Acon

Activate or Not: Learning Customized Activation

https://github.com/DecentMakeover/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/DecentMakeover/mdetect

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Deci-AI/super-gradients

Going Deeper with Convolutions

https://github.com/Deep-Learner/Plot_CNNs_Python_Latex

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

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