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

https://github.com/Kaushal28/Bengali-AI

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

https://github.com/Kaushalya/medclip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Kazuhito00/sahi-yolox-onnx-sample

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/KellyHwong/rethinking_generalization

Deep Residual Learning for Image Recognition

https://github.com/Kenneth-Wong/tf-faster-rcnn

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

https://github.com/KennithLi/Awesome-Zero-Shot-Object-Detection

Zero-Shot Object Detection by Hybrid Region Embedding

https://github.com/KentaItakura/CNN-classification-using-random-erasing-and-cut-out

Random Erasing Data Augmentation

https://github.com/KeremTurgutlu/papers

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/KernelA/pytorch-point-transformer

Point Transformer

https://github.com/Keshavkant/YOLO_Toll

YOLOv3: An Incremental Improvement

https://github.com/Kev1nZheng/yolov_mask

YOLOv3: An Incremental Improvement

https://github.com/KevinJ-Huang/BMNet

https://github.com/KevinKecc/MAL

Multiple Anchor Learning for Visual Object Detection

https://github.com/KeyG518/Unet_Cell_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Khalifa-2020/centernet1

Objects as Points

https://github.com/Khalifa-2020/centernet2

Objects as Points

https://github.com/KhusDM/PointNetTree

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

https://github.com/Kin-Zhang/mmfn

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/KingBoyBIT/yolov3test

YOLO9000: Better, Faster, Stronger

https://github.com/KingGugu/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Kinpzz/RCRNet-Pytorch

Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

https://github.com/KiranAkadas/My_Pointnet_v2

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

https://github.com/KiritoGH/frustum-pointnets

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

https://github.com/Kissonis/Quantum_test

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Knight825/models-pytorch

Squeeze-and-Excitation Networks

https://github.com/KnowledgeDiscovery/FaceSec

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/KookHoiKim/GroupFree3dBaseline

Group-Free 3D Object Detection via Transformers

https://github.com/KostadinovShalon/oln-mask

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Krupal09/MaskRCNN-Demo

Mask R-CNN

https://github.com/Kumara-Kaushik/proba_v_challenge

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

https://github.com/Kungbohan/EECSMed

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/KushaalShroff/Signature-and-Annotation-detection

Fast R-CNN

https://github.com/KwangHoonAn/Quantizations

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/KyeongmoonKim/sb

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

https://github.com/L-Reichardt/Cylinder3D-updated-CUDA

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

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