Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/inder9999/ISIC2017_skin_lesion_segmentation

Fully Convolutional Networks for Semantic Segmentation

https://github.com/infini8-13/MaskRCNN_tryout

Mask R-CNN

https://github.com/infinitemugen/Attention-Conv-Pytorch

Attention Augmented Convolutional Networks

https://github.com/inisis/caffe

SSD: Single Shot MultiBox Detector

https://github.com/innat/External-Attention-TensorFlow

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

https://github.com/innat/HybridModel-GradCAM

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/innat/VideoMAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/innat/VideoSwin

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/insigh/Faster-RCNN-Tensorflow

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/insigh/Relation_Network_for_Objection

Relation Networks for Object Detection

https://github.com/intelligent-vehicles/bevdriver

Pyramid Scene Parsing Network

https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset-Lukemia-Attri-MICCAI-2024

A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability

https://github.com/interestingzhuo/foodretreival

Deep Residual Learning for Image Recognition

https://github.com/ioanvl/1d_squeeze_excitation

Squeeze-and-Excitation Networks

https://github.com/iphyer/DefectDetection-MaskRCNN

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

https://github.com/ipmi-icns-uke/sparsam

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/irenazra/melanoma_segmentation_Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/irh-ca-team-car/attention-data

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/iriszero/DepthAwareCNNplus

Depth-aware CNN for RGB-D Segmentation

https://github.com/irmvlab/point-mamba

OctFormer: Octree-based Transformers for 3D Point Clouds

https://github.com/irvankadhafi/Burnt-PCB-Object-Detection

EfficientDet: Scalable and Efficient Object Detection

https://github.com/isaaccorley/hydro-foundation-model

Masked Autoencoders Are Scalable Vision Learners

https://github.com/isaaccorley/segmenter-pytorch

Segmenter: Transformer for Semantic Segmentation

https://github.com/isaachaw/GrabCarRecognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/isakdiaz/PlantNet_MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/isbrycee/gem-glass-segmentor

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/iset/iset3d

Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network

https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning

Deep Residual Learning for Image Recognition

https://github.com/iskandari/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/isl-org/Open3D-ML

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

https://github.com/islamosmanubc/MedMAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/issaiass/FacialMaskDetector

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

https://github.com/istvanmegyeri/audio_tagging

Focal Loss for Dense Object Detection

https://github.com/it6aidl/outdoorsegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ithuanhuan/gpu-py3-faster-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/ithuanhuan/py-fatser-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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