Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

124

보존된 평가 기록

977

코드

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

https://github.com/lim0606/caffe-googlenet-bn

Going Deeper with Convolutions

https://github.com/liminn/icnet-pytorch

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION

Deep Residual Learning for Image Recognition

https://github.com/lincaiming/py-faster-rcnn-update

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

https://github.com/lincaiming/py-faster-rcnn-windows

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

https://github.com/lindermanlab/S5

State Space Models for Event Cameras

https://github.com/lingtengqiu/Yolo_Nano

Bag of Freebies for Training Object Detection Neural Networks

https://github.com/lingyunwu14/STFT

Relation Distillation Networks for Video Object Detection

https://github.com/lingzhang1/pointnet_tensorflow

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

https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/linjiefu-u/mamba_dose

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/linjieli222/hero_video_feature_extractor

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/linmeeka/semanticSlam

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/linmeeka/slamProject

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/linxi159/context_aug

Modeling Visual Context is Key to Augmenting Object Detection Datasets

https://github.com/linzhiqiu/clip-flant5

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/lipengfeizju/Detection

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

https://github.com/lipengyuMachineLearner/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/lippman1125/maskrcnn_benchmark_mobilenetv2

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

https://github.com/lippman1125/mmdetection_lqy

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/liqiang2018/Vehicle_delector

SSD: Single Shot MultiBox Detector

https://github.com/liqiang2018/ssd_mobilenet1.5

SSD: Single Shot MultiBox Detector

https://github.com/lisilin013/ICNet-tensorflow-ros

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/littletomatodonkey/code_scipts

Activate or Not: Learning Customized Activation

https://github.com/liuch37/image-processing

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/liuch37/semantic-segmentation

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/liuji93/yolo_v3

YOLOv3: An Incremental Improvement

https://github.com/liujiyuan13/MAE-code

Masked Autoencoders Are Scalable Vision Learners

https://github.com/liuky74/caffe_liuky74

SSD: Single Shot MultiBox Detector

https://github.com/liulaomo/go-ai-related

Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

https://github.com/liulianjushi/yolo_v3_tf2.0

YOLOv3: An Incremental Improvement

https://github.com/liuliu408/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/liunian-harold-li/desco

DesCo: Learning Object Recognition with Rich Language Descriptions

https://github.com/liuqiang3/faster_rcnn

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

https://github.com/liuruiyang98/Jittor-MLP

AS-MLP: An Axial Shifted MLP Architecture for Vision

https://github.com/liuxiangchao369/VRS-training

YOLOv3: An Incremental Improvement

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