Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PaulCCCCCCH/Multimodal-Categorization-of-Crisis-Events-in-Social-Media
https://github.com/PaulEmmanuelSotir/DeepCV
https://github.com/PaulGitt/ShuffleNetV2-tensorflow
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/Pe4enIks/TrainableActivation
https://github.com/PedroPachuca/project1
Unsupervised Representation Learning by Predicting Image Rotations
https://github.com/Peihao-Xiang/Label-Ranker
https://github.com/PeiqinZhuang/API-Net
Learning Attentive Pairwise Interaction for Fine-Grained Classification
https://github.com/Pek20180909/SVHN
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://github.com/Pepitaw/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision
https://github.com/PericlesPet/darknet-demo
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/PetersonSilvadeJesus/yolov3-data-images
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/PhIMaL/DeePyMoD_torch
https://github.com/PhamNguyen97/TSA_pytorch
https://github.com/PhamQuocHuy1101/UDA-Image-classification
https://github.com/PhanTom2003/PSPnet
https://github.com/PhilChina/classifier-balancing
Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/Phimos/Paddle-Colorization
https://github.com/PierrickPochelu/word_tree_label
Decision Forests, Convolutional Networks and the Models in-Between
https://github.com/PistonY/ResidualAttentionNetwork
https://github.com/Piyushdharkar/Residual-Attention-Aware-Network
https://github.com/PlumedSerpent/tmp_perspective_map
https://github.com/PolarisAlpha/darknet
https://github.com/PolinaDruzhinina/mobilenet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/PolinaDruzhinina/modelnet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Pongpisit-Thanasutives/Multi-task-Physics-informed-neural-networks
https://github.com/Popcorn-sugar/Deep_v2
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/Popoooo/darknet_test
https://github.com/PotatoSpudowski/CactiNet
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/PowerOfDream/digitx
https://github.com/Prateek93a/DoodleAI
https://github.com/PratikTayshete/Cats-and-Dogs-Image-Classification
https://github.com/Praveen94/pytorch-encoding
https://github.com/Prev/downsampled-imagenet-path-fixer
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.