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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/airctic/icevision

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

https://github.com/airplane2230/keras_Model_for_Beginner

SSD: Single Shot MultiBox Detector

https://github.com/airplane2230/keras_SSD_for_beginner

SSD: Single Shot MultiBox Detector

https://github.com/aisinai/seeg_analysis

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/aitorzip/PyTorch-CycleGAN

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

https://github.com/ajayjain/vectorascent

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ajaystar8/PDRUNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/ajaystar8/UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajcohen1/CoVision-Social-Distance-Violation-Detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ajfisch/few-shot-cp

Prototypical Networks for Few-shot Learning

https://github.com/ajinkyakhoche/Object-Detection-Project

SSD: Single Shot MultiBox Detector

https://github.com/ajit2704/peexle

A Neural Algorithm of Artistic Style

https://github.com/ajithvallabai/getsetgo_keras-beginner

Going Deeper with Convolutions

https://github.com/ajjdan/KaI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajjdan/Karst-Segmentation-from-DEM

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajoshi944/Segmentation-severstal-steel

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ajoshiusc/qrsegment

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akalia77/hinet

HINet: Half Instance Normalization Network for Image Restoration

https://github.com/akamaster/pytorch_resnet_cifar10

Deep Residual Learning for Image Recognition

https://github.com/akanametov/NeuralStyleTransfer

Neural Style Transfer: A Review

https://github.com/akanametov/neural-style-transfer

Neural Style Transfer: A Review

https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101

Deep Residual Learning for Image Recognition

https://github.com/akashrajkn/micaugment

MicAugment: One-shot Microphone Style Transfer

https://github.com/akashrajkn/waffles-and-posteriors

Soft Weight-Sharing for Neural Network Compression

https://github.com/akhadangi/EM-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akhadangi/Multi-armed-Bandits

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/akinoriosamura/TorchSeg-mirror

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/akinsezer26/PersonDetection_TelegramBot

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/akkaze/cnn-without-any-downsampling

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/akommini/Spoken-Numeric-Digit-detection

Deep Residual Learning for Image Recognition

https://github.com/akrapukhin/MobileNetV3

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/akrsrivastava/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akshatgarg99/FewShotLearning

Prototypical Networks for Few-shot Learning

https://github.com/akshayb7/Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/aksub99/U-Net-Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/akwasigroch/Pretext-Invariant-Representations

Self-Supervised Learning of Pretext-Invariant Representations

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