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/Gavino7/YOLOv3set

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gedeon-m-gedus/compressed_object_detection

Compressed Object Detection

https://github.com/GeeGeeWP/SCML

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/GeekAlexis/Guardian

Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing

https://github.com/GenesisDCarmen/C_Reconocimiento_Facial

Scalable Object Detection using Deep Neural Networks

https://github.com/GeoDQ/Seismic-image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Georg030/MRI-Image-Denoising

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

https://github.com/George-Holbrow-Wilshaw/Human-Protein-Atlas-Kaggle

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

https://github.com/George-Ogden/Mask-RCNN

Mask R-CNN

https://github.com/GewelsJI/PNS-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Ggs1mida/Awesome-DataFusion

nuScenes: A multimodal dataset for autonomous driving

https://github.com/Ggs1mida/Awesome-PointCloudRegistration

nuScenes: A multimodal dataset for autonomous driving

https://github.com/GiaKhangLuu/YOLOv1_from_scratch

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

https://github.com/GianRomani/YOLOv3-Neural-Networks-project-

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

https://github.com/GiantPandaCV/yolov3-point

YOLOv3: An Incremental Improvement

https://github.com/GibranBenitez/IPN-hand

HarDNet: A Low Memory Traffic Network

https://github.com/Gideon0805/Tensorflow1.15-Model-Pruning

Squeeze-and-Excitation Networks

https://github.com/Gideon0805/Tensorflow_Model_Pruning

Squeeze-and-Excitation Networks

https://github.com/GinZhu/MIASSR

Enhanced Deep Residual Networks for Single Image Super-Resolution

https://github.com/GiovanniPasq/DA-Faster-RCNN

Domain Adaptive Faster R-CNN for Object Detection in the Wild

https://github.com/GitDT11/EDSR-pytorch

Enhanced Deep Residual Networks for Single Image Super-Resolution

https://github.com/GitHberChen/FCN-Pytorch

SSD: Single Shot MultiBox Detector

https://github.com/GitHubXlong/TecoGAN

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/Gitgigabyte/mmd

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Gitgigabyte/mmdetection_

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/GiulioFischietti/Tirocinio

YOLOv3: An Incremental Improvement

https://github.com/GivralNguyen/-Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU-

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/GivralNguyen/Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/Glp91/fire-detection

Deep Residual Learning for Image Recognition

https://github.com/GodofCCode/yolo3

YOLOv3: An Incremental Improvement

https://github.com/Godofnothing/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/Godofnothing/pi-GAN

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

https://github.com/Gojay001/toolkit-DeepLearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GordonnW/ShuffleNetV1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/Gowti-AiboT/GAN-based-SISR-Keras

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Graylab/deepH3-distances-orientations

Deep Residual Learning for Image Recognition

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