Discussion
=============

The DashCamNet system allows us to detect and recognize various objects such as cars, persons, road signs, and bicycles in different environments.
Through our examples, we were able to see how the system can be applied to a variety of different applications.

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            Group Discussion Points
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        <ul>
            <li> 
                Considering the limitations of DashcamNet, what improvements or additional features could be implemented to enhance its performance and broaden its applications?
            </li>
            <li>
                How would you adapt the DashcamNet model to better suit a specific use case or environment? 
                <ul>
                    <li>
                        To adapt the DashcamNet model for a specific use case, you would need to fine-tune it with data relevant to that task.
                        For instance, if you wanted to optimize the model for detecting objects in a non-US traffic scenario, you would need to collect and label dashcam videos from that region. Fine-tuning the model with this data would improve its performance and make it more suitable for the specific context.
                    </li>
                </ul>
            </li>
            <li>
                What potential issues or challenges might arise when using DashcamNet in real-world scenarios, such as varying lighting conditions or different traffic patterns?
                How can these challenges be addressed to ensure optimal performance?
            </li>
        </ul>
    </div>