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/Gahyeonkim09/AAPL

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Gaurav14cs17/YOLOE

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Gavino7/YOLOv3set

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gedeon-m-gedus/compressed_object_detection

Compressed Object Detection

https://github.com/GeeGeeWP/SCML

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/GeekAlexis/Guardian

Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing

https://github.com/GenesisDCarmen/C_Reconocimiento_Facial

Scalable Object Detection using Deep Neural Networks

https://github.com/GeoDQ/Seismic-image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/George-Holbrow-Wilshaw/Human-Protein-Atlas-Kaggle

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

https://github.com/George-Ogden/Mask-RCNN

Mask R-CNN

https://github.com/GewelsJI/PNS-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Ggs1mida/Awesome-DataFusion

nuScenes: A multimodal dataset for autonomous driving

https://github.com/Ggs1mida/Awesome-PointCloudRegistration

nuScenes: A multimodal dataset for autonomous driving

https://github.com/GhadeerElmkaiel/SegFormer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/GiaKhangLuu/YOLOv1_from_scratch

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

https://github.com/GianRomani/YOLOv3-Neural-Networks-project-

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

https://github.com/GiantPandaCV/yolov3-point

YOLOv3: An Incremental Improvement

https://github.com/GibranBenitez/IPN-hand

HarDNet: A Low Memory Traffic Network

https://github.com/Gideon0805/Tensorflow1.15-Model-Pruning

Squeeze-and-Excitation Networks

https://github.com/Gideon0805/Tensorflow_Model_Pruning

Squeeze-and-Excitation Networks

https://github.com/GiovanniPasq/DA-Faster-RCNN

Domain Adaptive Faster R-CNN for Object Detection in the Wild

https://github.com/GitHberChen/FCN-Pytorch

SSD: Single Shot MultiBox Detector

https://github.com/Gitgigabyte/mmd

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Gitgigabyte/mmdetection_

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/GiulioFischietti/Tirocinio

YOLOv3: An Incremental Improvement

https://github.com/GivralNguyen/-Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU-

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/GivralNguyen/Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU

Receptive Field Block Net for Accurate and Fast Object Detection

https://github.com/Glp91/fire-detection

Deep Residual Learning for Image Recognition

https://github.com/GodPater/model_fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/GodofCCode/yolo3

YOLOv3: An Incremental Improvement

https://github.com/Godofnothing/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/Gojay001/toolkit-DeepLearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GordonnW/ShuffleNetV1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/Graylab/deepH3-distances-orientations

Deep Residual Learning for Image Recognition

https://github.com/GregorKobsik/Octree-Transformer

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/Guanghan/CenterNet-Gluon

Objects as Points

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