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2D Classification

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

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/ChrominskiMateusz/LSTM_layer_with_kernel_fusion

The Matrix Calculus You Need For Deep Learning

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/Clemson-AI/p2gan

P$^2$-GAN: Efficient Style Transfer Using Single Style Image

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

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

https://github.com/CocoJam/MAML

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

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/CogComp/BenchmarkingZeroShot

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

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

https://github.com/ColdNoodler/py-faster-rcnn-cuda10

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

https://github.com/Coldestadam/DynamicHead

Dynamic Head: Unifying Object Detection Heads with Attentions

https://github.com/Coldmooon/RCNN-for-Latest-Caffe

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/Coldmooon/SSD-on-Custom-Dataset

SSD: Single Shot MultiBox Detector

https://github.com/CompVis/adaptive-style-transfer

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/CompVis/content-targeted-style-transfer

A Content Transformation Block For Image Style Transfer

https://github.com/Congpeiliang/yolo

YOLOv3: An Incremental Improvement

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