Papers with Code 아카이브
Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 124
- 보존된 평가 기록
- 977
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bok9504/UAV_MOT_LineRegistration
https://github.com/bolianchen/nuscenes_depth
https://github.com/bonlime/keras-deeplab-v3-plus
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/boom85423/Cartoon-style-Stickers-Generator
https://github.com/boom85423/Seq2seq-model-for-Meme-Generator
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/boris127/vehicle-detection
https://github.com/borisdayma/clip-jax
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bostondiditeam/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/bowensong0312/darknet
https://github.com/bowu1004/instance_segmentation_RealSense
https://github.com/boyellow/adaad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/bpuladi/Automated-PD-L1-Scoring
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/bradenhurl/DeepGTAV-PreSIL
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/GTAVisionExport-DepthExtractor
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/PreSIL-tools
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bragancas/vnet_pytorch-atriaseg2018
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/branislavhesko/segmentation_framework
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/brbzjl/pointnet2
https://github.com/brdiep113/UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/brightyoun/TITS-LPST
Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
https://github.com/brown-palm/ObjectPrompt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/brunasil98/u-net_Cancer
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bruthyu/bpt-vlm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/brycexu/MR-Residual-Net
https://github.com/brytsknguyen/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/bt77/pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/buaaYYC/faster-rcnn-tensorflow
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/buaazyz/battery_detect
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/bubbliiiing/yolov4-pytorch
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/bupt-ai-cz/LLVIP
https://github.com/bupt-ai-cz/hhcl-reid
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/buptdbj/darknet-windows-linux
https://github.com/burakalperen/Pytorch-Semantic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/busyboxs/Some-resources-useful-for-me
Training Region-based Object Detectors with Online Hard Example Mining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.