https://github.com/pwc-1/Paper-9/tree/main/5/yolos
YOLOSA: Object detection based on 2D local feature superimposed self-attention
Papers with Code 아카이브
Object Detection
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pwc-1/Paper-9/tree/main/6/AVA_hpa/src/RandAugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/pwc-1/Paper-9/tree/main/6/CME
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
https://github.com/pwc-1/Paper-9/tree/main/7/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/pwc-1/Paper-9/tree/main/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/pwc-1/Paper-9/tree/main/mamba
https://github.com/pwochner/cmr_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pydsgz/DeepVOG
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pytorch/vision
https://github.com/qa276390/searchtrack
https://github.com/qdezkaggle/DetectionTool
https://github.com/qdhill/openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/qdu1995/DQSD
https://github.com/qfgaohao/pytorch-ssd
https://github.com/qhur135/MinneApple
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
https://github.com/qhur135/MinneApple_Detection
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
https://github.com/qianfu1997/synchro_new
https://github.com/qianguih/voxelnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/qianpeisheng/Unet_semicon
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/qianyu-dlut/DiffDIS
High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity
https://github.com/qianyuzqy/TransVOD_plusplus
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
https://github.com/qiaopTDUN/mae-repo
https://github.com/qilei123/DEEPLAB_4_RETINA
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/DEEPLAB_4_RETINAIMG
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/DeformableConvV2
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/DeformableConvV2_crop
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/MLKP4OpenImage
Multi-scale Location-aware Kernel Representation for Object Detection
https://github.com/qilei123/RefineDet4OpenImagev4
https://github.com/qilei123/fpn_crop
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/fpn_crop_v1_5d
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/maskrcnn_benchmark_ROP
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/qilei123/pyfasterrcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/qilei123/sod_v1
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qilei123/sod_v1_demo
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/qingyuanchen1997/Bag-of-Tricks
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/qingyujean/Classification-on-imbalanced-data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.