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

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Video segmentation and annotation are essential tasks for deep learning (DL) applications that involve analyzing and understanding video content. For example, you might want to segment and annotate objects, people, actions, scenes, or events in videos for purposes such as surveillance, autonomous driving, sports analysis, or video editing. But how do you train and deploy video segmentation and annotation models for DL? In this article, we will cover some of the main steps and challenges involved in this process.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
Kunal Dargan

MS(Research) at IIT Delhi | Computer Vision | Surgical Datascience | Medical AI

1 Data preparation

The first step is to prepare your data for training and testing your models. This means collecting and organizing videos that are relevant to your problem domain and have sufficient quality and diversity. You also need to label your videos with the desired segmentation and annotation information, such as bounding boxes, masks, polygons, keypoints, or captions. Labeling videos can be time-consuming and labor-intensive, so you might want to use some tools or services that can help you automate or streamline this process. For example, you can use pre-trained models, active learning, or crowdsourcing to reduce the amount of manual annotation required.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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.

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Kunal Dargan

MS(Research) at IIT Delhi | Computer Vision | Surgical Datascience | Medical AI
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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.

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2 Model selection

The next step is to select a suitable model architecture and framework for your video segmentation and annotation task. There are many types of models and frameworks available for DL, each with its own advantages and disadvantages. For example, you can use convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, or graph neural networks (GNNs) to capture the spatial and temporal features of videos. You can also use frameworks such as TensorFlow, PyTorch, Keras, or MXNet to implement and train your models. You should consider factors such as the complexity, accuracy, speed, scalability, and compatibility of your models and frameworks when making your choice.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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

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3 Model training

The third step is to train your model on your data using a suitable optimization algorithm and loss function. This means finding the optimal parameters for your model that minimize the error between the predicted and the actual segmentation and annotation outputs. You also need to tune your hyperparameters, such as learning rate, batch size, epochs, or regularization, to avoid overfitting or underfitting your data. Training video segmentation and annotation models can be computationally intensive and require a lot of resources, such as memory, CPU, or GPU. Therefore, you might want to use some techniques or tools that can help you speed up or simplify this process. For example, you can use data augmentation, transfer learning, distributed training, or cloud services to enhance your training performance and efficiency.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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.

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4 Model evaluation

The fourth step is to evaluate your model on unseen data using appropriate metrics and benchmarks. This means measuring how well your model performs on new videos that were not used for training or validation. You also need to compare your model with other existing or state-of-the-art models on the same task or dataset. You should use metrics that reflect the quality and relevance of your segmentation and annotation outputs, such as precision, recall, F1-score, IoU, mAP, or BLEU. You should also use benchmarks that are widely used and accepted by the research or industry community, such as COCO, PASCAL VOC, KITTI, or YouTube-8M.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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.

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5 Model deployment

The final step is to deploy your model into a production environment where it can be used for real-world applications. This means integrating your model with the video sources, platforms, or systems that require your segmentation and annotation outputs. You also need to ensure that your model is robust, reliable, and secure in the deployment setting. You should consider factors such as the latency, bandwidth, scalability, compatibility, and privacy of your model and its outputs when deploying it. You should also monitor and update your model regularly to maintain its performance and accuracy.

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Madan Pernati

Director | Engineering, Product Strategy, Program Management, Service Delivery, Leadership, 0→1 CoE/GCC Setup
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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

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

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1 Data preparation

2 Model selection

3 Model training

4 Model evaluation

5 Model deployment

6 Here’s what else to consider

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