How do you train and deploy video segmentation and annotation models for DL?

Data preparation is one of the most important phases. Modern open-source pre-trained models can detect most common objects. In my experience, we can run specific video data with available models and evaluate their performance. Steps are below. #1 Fully groundtruthing the representative set of videos with the output you need #2 Run the application with the pre-trained model and store the model-aided detections/tracker outpu #3 Compare detector/tracker output against groundtruth to identify missed and inaccurate detection #4 Label the error data with tools like LabelMe, CVAT, other tools using step 03 data. #5 Fine-tune the model with new data. #6 Test the new model with footage not used in step #02 Loop can go on as needed.

Data preparation is vital for training deep learning models in video segmentation and annotation. It involves gathering diverse, quality videos in the domain and labelling them with info like bounding boxes or masks. Manual labelling is time-consuming, so tools like Labelme, Computer Vision Annotation Tool (CVAT), or even outsourcing through Mechanical Turk can be utilized to streamline the process. Tracking labels is crucial for some tasks. Standard formats like DAVIS 17 (coloured masks)or COCO VIS (JSON) are common for video annotation datasets. By using standardized data formats and leveraging annotation tools, it becomes more efficient, reducing the turnaround time of deep learning models for video segmentation and annotation.

Model selection could be based on multiple factors listed below - Accuracy levels needed for the business application - Target hardware platform you have in hand where you can create the model - Target hardware platform on which model has to perform - Number of parallel jobs model has to perform and hence the latency - One model detect all kinds of objects or a model pipeline is needed

Assuming we have the image files and annotation files ready, we'll need a hardware processing platform to build the model with the necessary parameter configurations. Also consider running a check on data for any cloaked images, duplicate images that could impact the overall model performance. Model training might take anywhere from a few hours to many days in some cases. Investing in the appropriate hardware as a fixed asset or utilising cloud hardware on demand could be considered. Once the model training begins, it is critical to monitor the progress to ensure that we do not encounter unsuccessful output towards the the end.

Model evaluation must be done at an early stage, rather than waiting for the outcomes of production deployment. This means that we need to have some isolated datasets for model validation to ensure that detection latency and accuracy are within acceptable limits. It is recommended to assess each model's performance against the same isolated dataset to easily verify previous performance advancements.

Typically, the model must be distributed across a variety of hardware platforms, some of which may differ from the one on which it was generated. To achieve the best results, the model installed on the target hardware should have the ideal parameter configuration. If models are maintained in a repository, they must be packaged with some sort of versioning on the target hardware platforms to facilitate upgrades and rollbacks. From a security aspect, model files should be safeguarded using encryption, obfuscation, and other applicable techniques that do not compromise actual performance.

Other important parts of the life cycle of model training and deployment for DL on videos are below. - Choosing the right annotation strategy to fulfil business requirements - Maintaining the image data sets that serve as the foundation for model building - Shortlisting the verified video data and collecting error cases for model enhancement - Choosing an appropriate tool for conducting annotation jobs - Linking the tool with the model generation life cycle - Groundtruthing the isolated video data for performance verification after model generation - Creating verification methods prior to model deployment - Installing and upgrading models on the production system through automation - Securing the model files on the production systems