Image Classification
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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/b-carter/overinterpretation
Overinterpretation reveals image classification model pathologies
https://github.com/b-faye/unsupervised-context-normalization
Training Faster by Separating Modes of Variation in Batch-normalized Models
https://github.com/b8goal/PIRL-Residual-network
https://github.com/ba-san/MobilePose-Pi
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/bacdavid/InfomaxVAE
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/baichuan-inc/Baichuan-Omni-1.5
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
https://github.com/baidu-research/DT-RAM
https://github.com/bailvwangzi/repulsion_loss_ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/baiyancheng20/gaga
https://github.com/baizhenmao95/2019-ZTE-Algorithm-Competition
DAC: Data-free Automatic Acceleration of Convolutional Networks
https://github.com/baky0905/hurricane-data-engineering
https://github.com/balajiselvaraj1601/Step_Dropout
https://github.com/balala8/FastViT_pytorch
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/balamurugan0714/TensorFlow
https://github.com/baldassarreFe/deep-koalarization
CNN Features off-the-shelf: an Astounding Baseline for Recognition
https://github.com/balshersingh10/Object-Recognition
https://github.com/bamos/densenet.pytorch
https://github.com/bamps53/convnext-tf
https://github.com/bangoc123/mlp-mixer
https://github.com/baohq1595/aae-experiment
https://github.com/bardank/dc-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/barisozmen/deepaugment
https://github.com/barmayo/spatial_attention
https://github.com/barnrang/Prototypical-network-keras-reimplementation
https://github.com/barrykidney/CarND-Behavioral-Cloning-P3
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/bartoszpanek1/Real-time-emotion-recognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/basile-bron/Artificial-Intelligence-Bible
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/baskargroup/Arboretum
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/baskargroup/biotrove
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/batmanlab/Ladder
LADDER: Language Driven Slice Discovery and Error Rectification
https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning
https://github.com/batuozt/gleam
https://github.com/batzner/indrnn
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/bazilas/matconvnet-ResNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.