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	ResNeXt	EfficientNet
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2017	2019
Twórca	Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.	Tan, M. & Le, Q. V.
Typ	Convolutional neural network with grouped/cardinality-based residual blocks	Compound-scaled convolutional neural network architecture
Źródło pierwotne	Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995.	Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.
Inne nazwy	ResNeXt, Aggregated Residual Transformations, grouped convolution residual network, cardinality-based ResNet	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2
Pokrewne	4	4
Podsumowanie	ResNeXt is a deep convolutional neural network architecture introduced by Xie, Girshick, Dollár, Tu, and He at CVPR 2017. It extends the residual network (ResNet) design by introducing a new architectural dimension called cardinality — the number of independent, parallel transformation paths within each residual block — enabling higher accuracy with fewer parameters and a simpler, more uniform design than its predecessors.	EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.