https://github.com/diasirish/mobilenetv3
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Improved Regularization of Convolutional Neural Networks with Cutout
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
Do Vision Transformers See Like Convolutional Neural Networks?
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.