# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 4 Image and Video Classification Algorithms on Mobile Devices
### EfficientNet
#### 4.2.3 EfficientNet-Lite  
EfficientNet-Lite is derived from the state-of-the-art EfficientNet architecture [51]. EfficientNet is a model that has achieved outstanding results in image recognition, speech recognition and video detection and improves the overall prediction/recognition accuracy. This model is suitable for deployment in devices with limited resources, such as mobile phones that implement EfficientNet-Lite, due to low resource consumption and low storage demand due to the low number of parameters needed to train the model. EfficientNet-Lite runs on all mobile CPUs/GPUs/EdgeTPUs [52].  
EfficientNet-Lite is based on the efficiency of EfficientNet and is used in edge devices, with five variants that vary in precision and size of the model (number of parameters). EfficientNet-Lite0 is the low-latency and low-size version of the model and, EfficientNet-Lite4 is the high-precision version.  
Figure 5 the red line represents the different versions of EfficientNet-lite and the blue line represents popular models for image classification (MobileNetV2, ResNet50 and InceptionV4). The comparison is made in terms of latency and accuracy is quantized with integers only and using the ImageNet dataset running in real time. As shown EfficientNet-Lite4, achieves an accuracy of 80.4% as does the InceptionV4 model, however the latter has a higher latency of approximately 80 ms. This last figure is significant because the processing [53].  
Table 4 shows some algorithms that perform well on mobile devices, analysing the input parameters and the output of each of the models in comparison with EfficientNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Version** & **Input Size (px)** & **\#Params** & **Accuracy** \\ \hline EfficientNetB0 & 224 \(\times\) 224 & 4,057,253 & 76.3\%/93.2\% \\ \hline EfficientNetB1 & 240 \(\times\) 240 & 6,582,914 & 78.8\%/94.4\% \\ \hline EfficientNet-B2 & 260 \(\times\) 260 & 7,777,012 & 79.8\%/94.9\% \\ \hline EfficientNet-B3 & 300 \(\times\) 300 & 10,792,746 & 81.1\%/95.5\% \\ \hline EfficientNet-B4 & 380 \(\times\) 380 & 17,684,570 & 82.6\%/96.3\% \\ \hline EfficientNet-B5 & 456 \(\times\) 456 & 28,525,810 & 83.3\%/96.7\% \\ \hline EfficientNet-B6 & 528 \(\times\) 528 & 40,973,969 & 84.0\%/96.9\% \\ \hline EfficientNet-B7 & 600 \(\times\) 600 & 64,113,049 & 84.4\%/97.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of features of EfficientNet models.  
The variants of the EfficientNet model shown in Table 3 were used to create the EfficientNet-Lite model, which can be implemented on mobile devices with unlimited resources and perform as well as the EfficientNet model for computers. Table 4 shows algorithms that, thanks to the low number of parameters used for their training, can be adapted to mobile phones; for example, MobileNet and MobileNetV2 achieve a similar accuracy of 70% and 71%, respectively. The number of parameters used is also relatively low, which means that the storage necessary for its operation is around 16 MB. Other algorithms, although they achieve similar performance to MobileNet, require a large amount of resources for processing. A special case is EfficientNet, which despite the higher number of parameters, has versions that can be adapted to a mobile device using TensorflowLite.