Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

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/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/azshue/TPT

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/b8goal/PIRL-Residual-network

Deep Residual Learning for Image Recognition

https://github.com/ba-san/MobilePose-Pi

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/backSpace001/PMR_WEB_APP1.0

YOLO9000: Better, Faster, Stronger

https://github.com/backseason/PoolNet

A Simple Pooling-Based Design for Real-Time Salient Object Detection

https://github.com/badrbmb/cities-watch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/baichuan-inc/Baichuan-Omni-1.5

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

https://github.com/bailvwangzi/repulsion_loss_ssd

SSD: Single Shot MultiBox Detector

https://github.com/baizhenmao95/2019-ZTE-Algorithm-Competition

DAC: Data-free Automatic Acceleration of Convolutional Networks

https://github.com/balancap/SSD-Tensorflow

SSD: Single Shot MultiBox Detector

https://github.com/balast/saliency_detector

A Simple Pooling-Based Design for Real-Time Salient Object Detection

https://github.com/bamps53/convnext-tf

A ConvNet for the 2020s

https://github.com/banben/maskrcnn-benchmark

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

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