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/leochangzliao/OPBM

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

https://github.com/leofansq/CBR

Calibration-free BEV Representation for Infrastructure Perception

https://github.com/leokarlin/StarNet

StarNet: towards Weakly Supervised Few-Shot Object Detection

https://github.com/leolee99/CLIP_ITM

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/leoliao2008/TensorflowMobileOfficialExample

Scalable Object Detection using Deep Neural Networks

https://github.com/leon-liangwu/MaskYolo_Caffe

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

https://github.com/leon-liangwu/py-caffe-yolo

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

https://github.com/leona-ha/Skin-Screening_Web-App

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/leonardhan1979/fasterRCNN

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

https://github.com/leonardoaraujosantos/seg_atrous

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/botnet

Bottleneck Transformers for Visual Recognition

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/coat

Co-Scale Conv-Attentional Image Transformers

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/convnext

A ConvNet for the 2020s

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/cotnet

Contextual Transformer Networks for Visual Recognition

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/davit

DaViT: Dual Attention Vision Transformers

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/edgenext

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientdet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/fastervit

FasterViT: Fast Vision Transformers with Hierarchical Attention

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gcvit

Global Context Vision Transformers

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gpvit

GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/halonet

Scaling Local Self-Attention for Parameter Efficient Visual Backbones

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera

Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hornet

HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/maxvit

MaxViT: Multi-Axis Vision Transformer

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family

An Image Patch is a Wave: Phase-Aware Vision MLP

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mobilenetv3_family

FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mobilevit

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/moganet

MogaNet: Multi-order Gated Aggregation Network

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/nat

Neighborhood Attention Transformer

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/pvt

PVT v2: Improved Baselines with Pyramid Vision Transformer

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/resnest

ResNeSt: Split-Attention Networks

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/swin_transformer_v2

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/uniformer

UniFormer: Unifying Convolution and Self-attention for Visual Recognition

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolor

You Only Learn One Representation: Unified Network for Multiple Tasks

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