Papers with Code 아카이브
Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mlzxy/devit
공식 코드https://github.com/mmaaz60/EdgeNeXt
공식 코드EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
https://github.com/mmaaz60/mvits_for_class_agnostic_od
공식 코드Class-agnostic Object Detection with Multi-modal Transformer
https://github.com/mmic-lcl/datasets-and-benchmark-code
공식 코드https://github.com/mmocking/riders
공식 코드https://github.com/mmoradi-iut/bodem
공식 코드Model-agnostic explainable artificial intelligence for object detection in image data
https://github.com/mmunir127/rapidnet-official
공식 코드RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
https://github.com/moabarar/qna
공식 코드https://github.com/mocherson/ImageGCN
공식 코드https://github.com/modeltc/eod
공식 코드https://github.com/modeltc/mqbench
공식 코드QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
https://github.com/modeltc/united-perception
공식 코드The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
https://github.com/mohamedtev/daca
공식 코드https://github.com/mohammedsb/harmony
공식 코드https://github.com/mohsenzand/objectbox
공식 코드ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
https://github.com/moinnabi/SelfPacedDeepLearning
공식 코드Self Paced Deep Learning for Weakly Supervised Object Detection
https://github.com/molden/probkt
공식 코드Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
https://github.com/momo1986/robust_sam_iv
공식 코드Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
https://github.com/mondalanindya/ICCVW2021_GSCEventMOD
공식 코드Moving Object Detection for Event-based Vision using k-means Clustering
https://github.com/monster-ghost/monodistill
공식 코드MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
https://github.com/moothes/a2s-usod
공식 코드https://github.com/moothes/a2s-v2
공식 코드Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
https://github.com/moothes/salod
공식 코드https://github.com/moured/document-graphics-digitization
공식 코드https://github.com/mousecpn/Boosting-R-CNN-Reweighting-R-CNN-Samples-by-RPN-s-Error-for-Underwater-Object-Detection
공식 코드Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection
https://github.com/mousecpn/boosting-r-cnn
공식 코드Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection
https://github.com/movingforward100/shadow_r
공식 코드ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer
https://github.com/moyog/fisheye8k
공식 코드FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
https://github.com/mpitropov/cadc_devkit
공식 코드https://github.com/mr-bigworth/mmca
공식 코드https://github.com/mrT23/TResNet
공식 코드https://github.com/mribrahim/PESMOD
공식 코드UAV Images Dataset for Moving Object Detection from Moving Cameras
https://github.com/mrnabati/CenterFusion
공식 코드CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
https://github.com/mrnabati/RRPN
공식 코드RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
https://github.com/mrqrs/cg-ssd
공식 코드CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.