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/MindSpore-scientific/code-14/tree/main/res_mlp_ms

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/MindSpore-scientific/code-14/tree/main/segmentation-dataset

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MindSpore-scientific/code-14/tree/main/weakly-supervised

Weakly Supervised Deep Detection Networks

https://github.com/MindSpore-scientific/code-2/tree/main/squeeze-and-attention-networks-for-semantic

Squeeze-and-Excitation Networks

https://github.com/MindSpore-scientific/code-2/tree/main/ssFPN

Feature Pyramid Networks for Object Detection

https://github.com/MindSpore-scientific/code-3/tree/main/corss-dataset-training

Cross-dataset Training for Class Increasing Object Detection

https://github.com/MindSpore-scientific/code-5/tree/main/polyloss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-7/tree/main/YOLOS

DETRs Beat YOLOs on Real-time Object Detection

https://github.com/MindSpore-scientific/code-7/tree/main/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/MindSpore-scientific/code-8/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-8/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MinghaoNJU/Object-detection-for-graphical-user-interface

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

https://github.com/Mingjian-Zhu/DyFPN

Dynamic Feature Pyramid Networks for Object Detection

https://github.com/MioChiu/MobileNet_V2_TensorFlow

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MioChiu/ResNet_TensorFlow

Deep Residual Learning for Image Recognition

https://github.com/Miracle1991/DetectionHub

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

https://github.com/MishaLaskin/vq-vae

Deep Residual Learning for Image Recognition

https://github.com/MishaLaskin/vqvae

Deep Residual Learning for Image Recognition

https://github.com/Mixture-of-Rookie/PASSL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/MoaazK/comp511-project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MoaazK/unet-transformer

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ModelBunker/PointNet-TensorFlow

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/ModelBunker/PointRCNN-PyTorch

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/ModelBunker/Stereo-RCNN-PyTorch

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/MohamedAbd0/darkflow-training

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

https://github.com/MohamedFarag21/DL-papers-implementation/blob/main/Computer_vision/UNET/Code/Unet_clean_version.py

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MohamedFarag21/DL-papers-implementation/tree/main/Computer_vision/CBAM:%20Convolutional%20Block%20Attention%20Module/Code

CBAM: Convolutional Block Attention Module

https://github.com/Mohammad-Asad-110/Fashion-Recommendation

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/MonsterPeng/Deformable-ConvNets-master

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/MonsterPeng/caffe_ssd

SSD: Single Shot MultiBox Detector

https://github.com/MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset

LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery

https://github.com/MoyoG/SHEL5K

https://github.com/MrAI-Rohan/SSDImplementation

SSD: Single Shot MultiBox Detector

https://github.com/MrD1360/Keras-implementation-of-MobileNetv3-with-L-RASSP

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MrGiovanni/UNetPlusPlus

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN

SSD: Single Shot MultiBox Detector

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