Papers with Code 아카이브
Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 124
- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tropcomplique/shufflenet-tensorflow
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/tropcomplique/shufflenet-v2-tensorflow
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/true-source/m2det-tf
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
https://github.com/truetqy/lesion_det_dual_att
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
https://github.com/trungmanhhuynh/Object-Detection-Localization
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
https://github.com/trupewate/lung_segmentation_tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/truskovskiyk/nima.pytorch
https://github.com/tryolabs/luminoth
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/trypag/pytorch-unet-segnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tsbiosky/Cascade-point-rcnn
https://github.com/tsdlrh/GHM_paddle
https://github.com/tsing-cv/DetNet
https://github.com/tsing-cv/EfficientNet-tensorflow-eager
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tsixta/jnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tsubasawb/DeepLearning_Paper
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/tsunekoromochi/ssd
https://github.com/tteepe/CenterNet-pytorch-lightning
https://github.com/ttppss/mmdetection
https://github.com/ttruty/facial-feature-mouse-control
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/ttt496/VisionTransformer
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/ttt496/vit-pytorch
https://github.com/tuan-datascience/cifar10
https://github.com/tuananh1007/Faster-R-CNN-and-Mask-R-CNN-in-PyTorch-1.0
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/tuanio/nextformer
https://github.com/tuanzhangCS/res2net-on-mxnet
https://github.com/tudelft-iv/view-of-delft-dataset
https://github.com/tuggeluk/DeepWatershedDetection
DeepScores and Deep Watershed Detection: current state and open issues
https://github.com/tuvovan/Unet-with-EfficientnetB7-Backbone
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/txin96/voxresnet
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
https://github.com/tyohei/chainerkfac
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/tyolm/groupfree3d_mindspore
https://github.com/tyunist/memory_efficient_mish_swish
https://github.com/tzing/t-cnn
Return of the Devil in the Details: Delving Deep into Convolutional Nets
https://github.com/tzodge/PCR-CMU
PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/u2seg/u2seg
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.