Papers with Code 아카이브

2D Classification

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

https://github.com/Asthestarsfalll/UniFormer-MegEngine

UniFormer: Unifying Convolution and Self-attention for Visual Recognition

https://github.com/Asthestarsfalll/VAN-MegEngine

Visual Attention Network

https://github.com/AtharvBhat/Plus-Ultra

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/Atharva-Phatak/Image2Image

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

https://github.com/Atharva-Phatak/PyTorch-Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/Atharva-Phatak/Season-Transfer

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

https://github.com/AtlasCoCo/Darknet

YOLO9000: Better, Faster, Stronger

https://github.com/Atom-min/MLBigWork

YOLOv3: An Incremental Improvement

https://github.com/Au3C2/GVS

Generator Versus Segmentor: Pseudo-healthy Synthesis

https://github.com/AutoVision-cloud/DeformablePVRCNN

Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

https://github.com/AutomatedAI/deeplab_inference

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/AutomatedAI/deeplab_segmentation_example

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Autonomous-Visual-Perception/detection-benchmark

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/AverageName/Cycle_gan_pytorch

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

https://github.com/AverageName/UI2IT

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

https://github.com/AviTewari/Lake-Ice-Monitoring

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Awia00/Predicted-Path-Tracing

Feature Pyramid Networks for Object Detection

https://github.com/AyushAniket/ResNET-and-various-normalizations

Deep Residual Learning for Image Recognition

https://github.com/BAJUKA/SQuAD-NLP

SQuAD: 100,000+ Questions for Machine Comprehension of Text

https://github.com/BBDrive/Meta-SGD-RL

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/BBuf/Darknet

YOLO9000: Better, Faster, Stronger

https://github.com/BIG-CHENG/FaceRec

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/BIGBALLON/distribuuuu

Bottleneck Transformers for Visual Recognition

https://github.com/BITCS-Information-Retrieval-2020/search-rattailcollagen1

Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!

https://github.com/BIYTC/mobilenet_maskrcnn

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/BMIRDS/deepslide

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

https://github.com/BOBrown/deeparsing-master

Pyramid Scene Parsing Network

https://github.com/BPMJG/annotated-F-pointnet

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/BPMJG/annotated_pointnet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/BPYap/IMT-Style-Transfer

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/PVTv2

PVT v2: Improved Baselines with Pyramid Vision Transformer

https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/Shuffle_Transformer

Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

https://github.com/BR-IDL/PaddleViT/blob/main/object_detection/DETR

End-to-End Object Detection with Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification

ConvMLP: Hierarchical Convolutional MLPs for Vision

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/CoaT

Co-Scale Conv-Attentional Image Transformers

https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt

A ConvNet for the 2020s

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