Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/hustvl/YOLOP

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/husty530/Yamashita-darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/huyhieupham/learning-from-multiple-annotators

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/huynhtuan17ti/AI4VN-Hackathon2020

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/huytranvan2010/RetinaNet-Understanding

Focal Loss for Dense Object Detection

https://github.com/huytranvan2010/SSD

SSD: Single Shot MultiBox Detector

https://github.com/huyz1117/Non_Local_Net_TensorFlow

Non-local Neural Networks

https://github.com/huzpsb/EffTransfer

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/hweersot/darknet-custom

YOLO9000: Better, Faster, Stronger

https://github.com/hwillard98/htmlify-yolo-training

YOLOv3: An Incremental Improvement

https://github.com/hx19940102/Image-Segmentation-based-on-FCN

Deep Residual Learning for Image Recognition

https://github.com/hy-signal/VoxelNet

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

https://github.com/hyang0129/foodclassapp

Focal Loss for Dense Object Detection

https://github.com/hycis/TensorGraph

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hydrogo/rainnet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hyh21521038/ssd-plate_detection

SSD: Single Shot MultiBox Detector

https://github.com/hysshy/hyv9_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hysts/pytorch_D-RISE

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hysts/pytorch_resnet

Deep Residual Learning for Image Recognition

https://github.com/hysts/pytorch_resnet_preact

Deep Residual Learning for Image Recognition

https://github.com/hyunjoonbok/Unet-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hz-ants/DenseFusion

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/hz-ants/Pose2Seg

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/hz-ants/apc-vision-toolbox

Fully Convolutional Networks for Semantic Segmentation

https://github.com/hz-ants/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hz2538/iot_JennyGo

Deep Residual Learning for Image Recognition

https://github.com/hzlbbfrog/generative-bim

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/iArunava/ResNet

Deep Residual Learning for Image Recognition

https://github.com/iacercalixto/butd-image-captioning

Are scene graphs good enough to improve Image Captioning?

https://github.com/iamilyasedunov/key_word_spotting

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/iamsingh/darknet

YOLOv3: An Incremental Improvement

https://github.com/iantimmis/DropBlock-Keras-Implementation

DropBlock: A regularization method for convolutional networks

https://github.com/iarai/landslide4sense-2022

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/iarai/weather4cast

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ibaiGorordo/ONNX-HybridNets-Multitask-Road-Detection

HybridNets: End-to-End Perception Network

https://github.com/ibaiGorordo/ONNX-Object-Localization-Network

Learning Open-World Object Proposals without Learning to Classify

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