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/vadimkantorov/contextlocnet

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

https://github.com/vahidkhosh/squeezeDet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/vahidkhosh/squeezedet-keras

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/vaibhavmit074/remove-bg-2

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/val-iisc/flss

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/valentina-kustikova/dnn-object-detectors-comp

SSD: Single Shot MultiBox Detector

https://github.com/valeoai/LOST

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/valeoai/VideoActionModel

The Cityscapes Dataset for Semantic Urban Scene Understanding

https://github.com/valeoai/found

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/valeoai/pointbev

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

https://github.com/vamshi4067/Anomaly-detection-using-computer-vision

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/vamsi3/pix2pix

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vandangorade/BrandLOGO_detection

SSD: Single Shot MultiBox Detector

https://github.com/vankhoa21991/medicalImgSEg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks

Non-local Neural Networks

https://github.com/vantupham/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/varshaneya/Res-SE-Net

Deep Residual Learning for Image Recognition

https://github.com/varunasthana92/YOLO_v3_implementation_Tensorflow2

YOLOv3: An Incremental Improvement

https://github.com/vasgaowei/TS-CAM

Conformer: Local Features Coupling Global Representations for Visual Recognition

https://github.com/vasgaowei/ts-cam-voc

Conformer: Local Features Coupling Global Representations for Visual Recognition

https://github.com/vasukumar92/Autonomous-driving---Car-detection

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/vayuvegula/kerasModels

Deep Residual Learning for Image Recognition

https://github.com/vbelz/Speech-enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vdurnov/xview2_1st_place_solution

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/venkai/RBDN

ImageNet Large Scale Visual Recognition Challenge

https://github.com/ver0z/Deformable-DETR-

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/vetlebh/unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vg-sravan/Cycle-Consistent-Generative-Adversarial-Networks

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/vgangal101/resnet_models

Deep Residual Learning for Image Recognition

https://github.com/vicoslab/cedirnet-3dof

CenterNet++ for Object Detection

https://github.com/victordibia/handtracking

SSD: Single Shot MultiBox Detector

https://github.com/victoria-lokteva/Performance-Monitoring-of-Object-Detection

Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment

https://github.com/victorpham1997/Local_ICNet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/vierachen/maskrcnn

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

https://github.com/vietanhdev/centernet-bdd-data

Objects as Points

https://github.com/vietanhdev/centernet-bdd-data-onnx-conversion

Objects as Points

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