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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/A-Mulvihill/TCD-Computer-Vision-Project-2023

Anchor-based Plain Net for Mobile Image Super-Resolution

https://github.com/A-PolarBear/GoogLeNet

Going Deeper with Convolutions

https://github.com/A-Telfer/AugKey

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/A511-1103/Express_detection

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

https://github.com/ACheun9/Pytorch-implementation-of-Mobile-Former

Mobile-Former: Bridging MobileNet and Transformer

https://github.com/AD2605/Action-Recognition

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AD2605/Single-Image-Super-Resolution

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

https://github.com/AGroupofProbiotocs/YOLO_V2_in_Tensorflow

YOLO9000: Better, Faster, Stronger

https://github.com/AI-Huang/SENet

Squeeze-and-Excitation Networks

https://github.com/AI-liu/Complex-YOLO

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/AICIP-UTK/spacenet-round3

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AIOSYM/dl-model-gallery

Going Deeper with Convolutions

https://github.com/AKASH2907/bird-species-classification

Mask R-CNN

https://github.com/AKASH2907/bird_species_classification

Mask R-CNN

https://github.com/ANSHUMAN87/Bias-Correction

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/ARandomOWL/window-gaze

PuRe: Robust pupil detection for real-time pervasive eye tracking

https://github.com/AVAuco/ssd_people

SSD: Single Shot MultiBox Detector

https://github.com/Aal-Izz-Well/pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/Aanisha/SuperResolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Aarav-Patel/darknet

YOLOv3: An Incremental Improvement

https://github.com/AbdulMoqeet/MAFFSRN

Multi-Attention Based Ultra Lightweight Image Super-Resolution

https://github.com/Abdulrahman-Adel/Object-Detection

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

https://github.com/AbdulrahmanCE/-pneumonia-detection-

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

https://github.com/Abhi-899/YOLOV4-Custom-Object-Detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Abhijeet2396/darknet

YOLOv3: An Incremental Improvement

https://github.com/AbhinavJhanwar/face_detection

YOLOv3: An Incremental Improvement

https://github.com/AbhinavJhanwar/object-detection-classification

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

https://github.com/AbhinayReddyYarva/SuperResolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Abhisek-/AlexNet

ImageNet Large Scale Visual Recognition Challenge

https://github.com/AceCoooool/DSS-pytorch

Deeply supervised salient object detection with short connections

https://github.com/AceCoooool/NLDF-pytorch

https://github.com/Adam123wu/Edge-YOLO_algorithm

YOLOv3: An Incremental Improvement

https://github.com/Adarol/u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AdeelH/pytorch-multi-class-focal-loss

Focal Loss for Dense Object Detection

https://github.com/Adelaide-AI-Group/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

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