# Effect of Camera Choice on Image-Classification Inference
## 5 Conclusions  
The primary objective of this research [47] was to determine whether and to what extent the camera employed to capture an image affects the image-classification output. The study employed a custom dataset of 4650 acquired images from six different cameras to demonstrate that the output of an image classifier during inference can depend significantly upon the specific camera that is used to acquire the image. This was shown both using graphical methods and by calculating Cohen's Kappa \(\kappa\) on the top predictions made by the same model on compatible images acquired by different cameras. This observed dependency is the case even when the foreground principal object in a scene is easily recognizable by a human observer and when the cameras employed have a similar FOV. The extent of the dependence of the output of an image classifier upon the camera for a similar FOV is a function of the scenario and in particular, the background to the foreground object and the ambient lighting. In this exploratory study, we found that the dependence upon the camera is relatively small when there is a plain background to the foreground object. That is, the object class with the highest prediction probability is usually not affected by the specific camera used to acquire the image, but the prediction probabilities of the various object classes in the classification ranking do show some dependence on the camera. However, when the background is more complex, the object class with the highest prediction probability can sometimes be different depending upon the specific camera used to acquire the image. It should be stressed that these results are more significant given that there are some elements of the image-classification ecosystem that inherently suppress the effect of different cameras:  
* One function of the camera ISP is to correct for camera-specific degradations such as lens distortion. Of course, the ISP can never fulfill this function perfectly, and the quality of the ISP correction will depend upon the price of the camera, but the ISP still regularizes the image acquired at the source to some extent. * During model training, validation, and testing, images from many different cameras will typically be used, so there is already an in-built level of data augmentation and diversity as far as cameras are concerned. This is certainly the case when models trained with the ImageNet-1k database are used for inference, as in this study, although the exact cameras represented are unknown or at least publicly unavailable. Since there are many important applications of image-classification inference, such as industrial inspection, medical diagnosis, emotion recognition [48], and autonomous \begin{table}
\begin{tabular}{c c} \hline \hline
**Range of Cohen’s Kappa \(\kappa\)** & **Strength of Agreement** \\ \hline \(<\)0.00 & Poor \\ \hline
0.00–0.20 & Slight \\ \hline
0.21–0.40 & Fair \\ \hline
0.41–0.60 & Moderate \\ \hline
0.61–0.80 & Substantial \\ \hline
0.81–1.00 & Almost Perfect \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Strength of agreement for ranges of Cohen’s Kappa \(\kappa\). vehicle operation, there is a question about how to minimize the effect of the specific camera employed on the inference in the future. There are possible solutions both in how images are acquired during model training and during inference. For model training, a systematic plan can be employed to acquire training images from a vast array of cameras rather than relying on the current incidental diversity approach. However, this may not be realistic, as acquiring sufficient training images and training models is already a very lengthy and expensive exercise. In addition, this would not safeguard against the use of new cameras for inference that were not available at the time the model was trained. As an alternative, a generic trained model can be customized for use with a specific camera by capturing a relatively small number of training images using the camera and then optimizing the generic model with further training.