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/euske/miniyolo

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

https://github.com/evaristr/py-faster_rcnn

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

https://github.com/evarsha/YOLO-Object-Detection

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

https://github.com/ewinata/CIFAR-10

Deep Residual Learning for Image Recognition

https://github.com/exekudos/se-resnet

Squeeze-and-Excitation Networks

https://github.com/eyangch/caloreasyv2

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/eynaij/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/eynaij/mmdetection_he

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/fL0n9/SKFAC-MindSpore

Deep Residual Learning for Image Recognition

https://github.com/fabiofumarola/ultrayolo

Deep Residual Learning for Image Recognition

https://github.com/fabriziocacicia/DeblurGAN-TF2.0

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/fabxy/accessibilityCV

Deep Residual Learning for Image Recognition

https://github.com/facebook/fb-caffe-exts

Going Deeper with Convolutions

https://github.com/facebook/fb.resnet.torch

Going Deeper with Convolutions

https://github.com/facebookarchive/fb.resnet.torch

Going Deeper with Convolutions

https://github.com/facebookresearch/3detr

An End-to-End Transformer Model for 3D Object Detection

https://github.com/facebookresearch/ClassyVision

Squeeze-and-Excitation Networks

https://github.com/facebookresearch/DomainBed

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/EgoTV

AI2-THOR: An Interactive 3D Environment for Visual AI

https://github.com/facebookresearch/LaMCTS

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

https://github.com/facebookresearch/WSL-Images

Exploring the Limits of Weakly Supervised Pretraining

https://github.com/facebookresearch/augmentation-corruption

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

https://github.com/facebookresearch/brainmagick

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/facebookresearch/clip-rocket

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/facebookresearch/deepmask

Learning to Segment Object Candidates

https://github.com/facebookresearch/detectron2

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

https://github.com/facebookresearch/detectron2/tree/master/projects/TridentNet/

Scale-Aware Trident Networks for Object Detection

https://github.com/facebookresearch/maskrcnn-benchmark

Mask R-CNN

https://github.com/facebookresearch/multimodal

Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

https://github.com/facebookresearch/multipathnet

A MultiPath Network for Object Detection

https://github.com/facebookresearch/perception_models

Perception Encoder: The best visual embeddings are not at the output of the network

https://github.com/facebookresearch/ppuda

A ConvNet for the 2020s

https://github.com/facebookresearch/pycls

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/stochastic_gradient_push

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/swav

End-to-End Object Detection with Transformers

https://github.com/facebookresearch/vip-mae

Masked Autoencoders Are Scalable Vision Learners

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