Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 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/eps696/aphantasia
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/eric-mitchell/macaw-min
Offline Meta-Reinforcement Learning with Advantage Weighting
https://github.com/ericjang/odin
https://github.com/ericyinyzy/vlattack
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fabiofumarola/ultrayolo
https://github.com/facebook/fb-caffe-exts
https://github.com/facebook/fb.resnet.torch
Some Improvements on Deep Convolutional Neural Network Based Image Classification
https://github.com/facebookarchive/fb.resnet.torch
Some Improvements on Deep Convolutional Neural Network Based Image Classification
https://github.com/facebookresearch/DomainBed
Domain Generalization for Object Recognition with Multi-task Autoencoders
https://github.com/facebookresearch/SparseConvNet
https://github.com/facebookresearch/augmentation-corruption
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/semi-supervised-ImageNet1K-models
Billion-scale semi-supervised learning for image classification
https://github.com/facebookresearch/vissl
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/facundoq/transformational_measures_experiments
https://github.com/fastscience-ai/medflamingo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fenghz/cosda
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
https://github.com/fengjiqiang/pretrainedmodel_pytorch
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filipbasara0/matryoshka-representation-learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fiveai/MoCaE
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/flyyufelix/DenseNet-Keras
https://github.com/forcesh/SupContrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/frank-roesler/Image_Segmentation
https://github.com/fulviomascara/pytorch-cv
Facial Expression Recognition Research Based on Deep Learning
https://github.com/funalab/PredictMovingDirection
https://github.com/fvisin/reseg
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
https://github.com/fxfviolet/Densenet_for_quiz_classification
https://github.com/gaetandi/cheXpert
https://github.com/gargarchit/60DaysOfUdacity
https://github.com/gfoxx29/Image-Captioning
https://github.com/ggoupy/SpikingConvNet
STDP-based spiking deep convolutional neural networks for object recognition
https://github.com/ghif/drcn
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.