# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 4 Discussion  
The results achieved in this study indicate clearly the power of SqueezeNet CNNs in extracting highly descriptive features from images, despite its small size. The improvements that we have proposed for the architecture enhanced the results significantly compared to the typical techniques for utilizing a pre-trained CNN. For example, a pre-trained GoogLeNet has been proposed in [13], but the method of fine-tuning it did not produce good results [13]. The reasons for this are several: 1) GoogLeNet has more weights (more than seven million), which requires larger datasets for training, and 2) GoogLeNet requires that the images be of size \(224\times 224\), which means all images have to be resized from the original size of \(640\times 480\) to \(224\times 244\). This modifies the aspect ratio of the images and causes deterioration in the classification accuracy of the network. When it comes to the SqueezeNet architecture used in this study, no resizing of images is needed. Table 3 presents the AVG scores achieved by Model 3 for all object classes and all datasets where the CONV2D layer size is fixed to 256 and \(T_{p}=0.3\). In general, some objects are harder to recognize because of their low occurrence number in the datasets. For example, if we look at object 10 (class of people) in the Urento1 dataset, we can see that it appears only once in the whole dataset. This explains the recognition rate of 50% achieved, because with only one example of this object, the network achieves a rate equal to random guessing. In the Urento2 dataset, the same class of objects (the \"people\" object) appeared slightly more often (8 times) and was recognized 66% of the time only. Therefore, another reason for the low score for this object class is that it is a complex object with high variability, making it difficult to detect without a large number of training samples. However, for other objects and despite the low number of occurrence for some of them, their recognition rate is mostly above 80% (see Figure 14). This is actually due to the fact that most of the objects are man-made and have low intra-class variability in their appearance, especially with the same dataset coming from the same building. This is also explained by the power of the pre-trained CNN to transfer knowledge from an auxiliary domain with a large dataset to another domain with a smaller dataset. In the Urento datasets, there are large barrel geometric distortions of the images. In our opinion, this partly explains the lower AVG scores achieved on these datasets compared to the KSU datasets. For example, in Figure 9, one can observe that the \"Board\" objects are sometimes located on the edges/corners of the image and hence become distorted, making them harder for the network to recognize. This partly explains the relatively lower AVG score achieved for this object, namely 85.90% and 79.96% in the Urento1 and Urento2 dataset, respectively, despite their high number of occurrences. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Method** & **SEN** & **SPE** & **AVG** & **Time(s)** \\ \hline \multirow{4}{*}{**UTrento2**} & SURF + GPR [9] & 77.72 & 100.00 & 88.86 & 1.17 \\ \cline{2-6}  & EDCS + GPR [9] & 70.00 & 90.12 & 80.06 & 1.2 \\ \cline{2-6}  & MR random projection [13] & 77.18 & 91.41 & 84.30 & 0.037 \\ \cline{2-6}  & Pre-trained GoogLeNet [13] & 67.27 & 98.55 & 82.91 & 0.066 \\ \cline{2-6}  & AE fusion of HOG + BOW + LBP [12] & 90.00 & 90.12 & 90.06 & 0.022 \\ \cline{2-6}  & Fine-tune improved SqueezeNet [ours] & 94.91 & 89.97 & 92.44 & 0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._