Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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보존된 평가 기록

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코드

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

https://github.com/BililiCode/ARGD

Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

https://github.com/BillyGun27/mobilenet_model

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

https://github.com/BingfengYan/DS_OCSORT

Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning

Mask R-CNN

https://github.com/BioMedIA-MBZUAI/Echocardiography-Assess

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BioWar/Satellite-Image-Segmentation-using-Deep-Learning-for-Deforestation-Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BlackAngel1111/Fast-RCNN

Fast R-CNN

https://github.com/BlackAngel1111/Yolo3_Pytorch

YOLOv3: An Incremental Improvement

https://github.com/Blaizzy/BiSeNet-Implementation

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/Bo396543018/picodet_repro

Instances as Queries

https://github.com/BobMcDear/pytorch-efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Boese0601/DUT_AI_2020_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/BogiHsu/Voice-Conversion-PyTorch

Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations

https://github.com/BonJunKu/ScooterHelmetDetector

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

https://github.com/Booomshaker/SparseRCNNWSL

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

https://github.com/Bribak/DeepConnection

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/BrilliantYuKaimin/XCiT-PaddlePaddle

XCiT: Cross-Covariance Image Transformers

https://github.com/Brunogomes97/Imdb

Random Erasing Data Augmentation

https://github.com/BryanPlummer/Two_branch_network

Revisiting Image-Language Networks for Open-ended Phrase Detection

https://github.com/BryanPlummer/cite

Revisiting Image-Language Networks for Open-ended Phrase Detection

https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x

Mask R-CNN

https://github.com/Burf/EfficientNet-Lite-Tensorflow2

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Burf/HRNetV2-OCR-Tensorflow2

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/Burf/ResNeSt-Tensorflow2

ResNeSt: Split-Attention Networks

https://github.com/Burf/ResNet-Tensorflow2

Deep Residual Learning for Image Recognition

https://github.com/Burf/SwinTransformer-Tensorflow2

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Burf/tfdetection

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

https://github.com/BurhanUlTayyab/DetectGPT

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

https://github.com/BurnhamG/PyTorch-ScalableFHVAE

Scalable Factorized Hierarchical Variational Autoencoder Training

https://github.com/Burton2000/semantic_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/C677/Machine-learning

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

https://github.com/CAMMA-public/ai4surgery

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/CAPTAIN-WHU/DOTA_devkit

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

https://github.com/CASIA-IVA-Lab/ISP-reID

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/CASOS-IDeaS-CMU/Detection-and-Discovery-of-Misinformation-Sources

A Topic-Agnostic Approach for Identifying Fake News Pages

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