Papers with Code 아카이브

16k

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/ChengBinJin/U-Net-TensorFlow

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ChengLeiWei/garbage-detect-using-YOLOv3-pytorch

YOLOv3: An Incremental Improvement

https://github.com/ChengWeiGu/ResNeSt-Pytorch

ResNeSt: Split-Attention Networks

https://github.com/Chenglin-Yang/LESA_classification

Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms

https://github.com/Chenglin-Yang/LESA_detection

Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms

https://github.com/ChenyangLEI/deep-video-prior

Neural Radiance Flow for 4D View Synthesis and Video Processing

https://github.com/Chenzixi1/D4LCNpaddle

Learning Depth-Guided Convolutions for Monocular 3D Object Detection

https://github.com/ChestnutLi/tf-faster-rcnn

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

https://github.com/ChetanTayal138/HAR-Web

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

https://github.com/ChielBruin/ros_faster_rcnn

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

https://github.com/ChintanThacker/Facial_Expression_EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Chris-On-PC/darknet

YOLOv3: An Incremental Improvement

https://github.com/Chris10M/RFB-Text-Detection

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/ChrisMats/Res2Net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Chrisding/cityscapes-preprocess

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/Chrisding/sbd-preprocess

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/Chrisding/seal

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/ChristianMarzahl/ObjectDetection

Focal Loss for Dense Object Detection

https://github.com/ChristophReich1996/MaxViT

MaxViT: Multi-Axis Vision Transformer

https://github.com/ChristophReich1996/Swin-Transformer-V2

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/Chubbyman2/SSD_MobileNet_Hand_Tracker

SSD: Single Shot MultiBox Detector

https://github.com/ChujoHiroto/tensorflow-ssd

SSD: Single Shot MultiBox Detector

https://github.com/ChunML/ssd-tf2

SSD: Single Shot MultiBox Detector

https://github.com/Cjiangbpcs/cjiang.github.io

YOLOv3: An Incremental Improvement

https://github.com/Cjl-MedSeg/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Cjmmmmmm/BreastUltrasound_Detection

Objects as Points

https://github.com/CoCoPIE-Pruning/CoCoPIE-ModelZoo

YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Code-0x00/caffe_windows

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/Code-Fight/darknet_study

YOLO9000: Better, Faster, Stronger

https://github.com/CodeChefVIT/VOID

Deep Residual Learning for Image Recognition

https://github.com/CodePlay2016/BiSENet-TF

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/CoinCheung/BiSeNet

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/CoinCheung/SphereReID

Random Erasing Data Augmentation

https://github.com/CoinCheung/denseCL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/CoinCheung/pytorch-loss/blob/master/pytorch_loss/iou_loss.py

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

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