Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 9
- 보존된 평가 기록
- 31
Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shunk031/simple-aesthetics-predictor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sidwa/ae_thesis
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/sigeek/mldl-rgbd-da
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/sigeek/rgbd-da-project
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/simpleintel/jupyter
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
https://github.com/sincerass/mvlpt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sithu31296/simple-object-tracking
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/smidtfab/nba_score_analyzer
https://github.com/soumith/convnet-benchmarks
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://github.com/soumith/imagenet-multiGPU.torch
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://github.com/sparvangada/capstone_project
https://github.com/spijkervet/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/ssrp/Multi-level-DCNet
Dense and Diverse Capsule Networks: Making the Capsules Learn Better
https://github.com/ssumin6/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/starfoe/Eye-bnb
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/sthalles/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/stomachcold/hctransformers
https://github.com/sudhakaranjain/3D_DEN
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains
https://github.com/sunformoon/ObjectRecognitionWithWithoutObjects
https://github.com/sunjesse/SimCLR_pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/super-wcg/Relation-Network-for-Object-Detection
https://github.com/swetak20/Deep_into_CNNs
https://github.com/swift-n-brutal/syntex
https://github.com/swxhss/densenet_tensorflow
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/sylviesss/pytorch-simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/szagoruyko/imagine-nn
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/ta9ryuWalrus/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/tailhq/dynaml
https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/takyamamoto/Local-Predictive_Coding_Network-with_Chainer
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition
https://github.com/talipucar/PyFlow_SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/taylor-914/DenseNet
https://github.com/tdhock/feature-learning-benchmark
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.