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/autoliuweijie/FastBERT

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/autonomousvision/navsim

TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving

https://github.com/avanetten/avanetten.github.io

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

https://github.com/avanetten/yolt

YOLO9000: Better, Faster, Stronger

https://github.com/avasalya/singleshot6Dpose

Squeezed Deep 6DoF Object Detection Using Knowledge Distillation

https://github.com/avinash31d/paper-implementations/tree/main/mobile_vit

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/aviros/pointnet_totations

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

https://github.com/aviros/roatationPointnet

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

https://github.com/avisekiit/wacv_2019

Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images

https://github.com/avm-debatr/bagganhq

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/avm-debatr/ganecdotes

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/avocardio/resnet_vs_convnext

Deep Residual Learning for Image Recognition

https://github.com/aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/awsaf49/gcvit-tf

Global Context Vision Transformers

https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws

Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources

https://github.com/axmoyal/Steganalysis-with-CNN

Deep Residual Learning for Image Recognition

https://github.com/ayanglab/swinganmr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/ayanglab/swinmr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/ayoolaolafenwa/PixelLib

Mask R-CNN

https://github.com/ayoolaolafenwa/orangelib

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ayoub-sudo/Attention-Unet-web-app

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ayoungkang/pytorch_Yolov4_lane-detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ayoungkang/yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ayush111111/cone_keypoint_regression

Real-time 3D Traffic Cone Detection for Autonomous Driving

https://github.com/ayushdabra/drone-images-semantic-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ayushdabra/dubai-satellite-imagery-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ayushdabra/lunar-landscape-images-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ayushmankumar7/Fast-Segementation

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/ayushmankumar7/SegNet---Tensorflow-2

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/ayushpalak/darknet_cokebottles

YOLO9000: Better, Faster, Stronger

https://github.com/ayushpalak/darknet_multi_cokebottles

YOLO9000: Better, Faster, Stronger

https://github.com/azamatkhid/mnasnet-pytorch

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/azibit/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/azraelzhor/tf-FCN

Fully Convolutional Networks for Semantic Segmentation

https://github.com/azshue/TPT

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/azy64/Deep-Learning

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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