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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/PiseyYou/MobileNet_Caffe_Configuration

SSD: Single Shot MultiBox Detector

https://github.com/PistonY/ResidualAttentionNetwork

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/PlumedSerpent/tmp_perspective_map

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PointsCoder/ONCE_Benchmark

One Million Scenes for Autonomous Driving: ONCE Dataset

https://github.com/PointsCoder/Pyramid-RCNN

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

https://github.com/PointsCoder/VOTR

Voxel Transformer for 3D Object Detection

https://github.com/PolarisAlpha/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/PolinaDruzhinina/mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/PolinaDruzhinina/modelnet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/PolinaYA/yolov3

YOLOv3: An Incremental Improvement

https://github.com/Popcorn-sugar/Deep_v2

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Popoooo/darknet_test

YOLO9000: Better, Faster, Stronger

https://github.com/Poseidon0711/Art-Generation-with-Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/Poseidon0711/Autonomous-driving-application---Car-detection

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

https://github.com/PotatoSpudowski/CactiNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/Pranav21091996/Semantic_Fidelity-and-Egoshots

nocaps: novel object captioning at scale

https://github.com/Pritam-N/ParNet

Non-deep Networks

https://github.com/ProGamerGov/neural-style-pt

A Neural Algorithm of Artistic Style

https://github.com/ProhetTeam/QuantMmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/ProofByConstruction/texture-networks

A Neural Algorithm of Artistic Style

https://github.com/PuchatekwSzortach/face_detection

Multi-view Face Detection Using Deep Convolutional Neural Networks

https://github.com/PuchatekwSzortach/voc_ssd

SSD: Single Shot MultiBox Detector

https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation

Deep Residual Learning for Image Recognition

https://github.com/PurabPatel555/Pytorch-UNet-Brain-MRI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Purkialo/CrowdDet

Detection in Crowded Scenes: One Proposal, Multiple Predictions

https://github.com/Pxtri2156/AdelaiDet_v2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu

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

https://github.com/Pythonista7/Implementing_

A Neural Algorithm of Artistic Style

https://github.com/QLinhHub/Image-super-resolution

Residual Dense Network for Image Restoration

https://github.com/QXL4515/CAGFuzz

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

https://github.com/Qdriving/Swin-TransformerV2-in-PP

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn

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

https://github.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/NanoDet-ncnn-Jetson-Nano

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

https://github.com/Qengineering/NanoDet-ncnn-Raspberry-Pi-4

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

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