At the core of excellent computer vision is quality data. But, how do you navigate when your data is not enough?
Worry no more! This article will get into the details of some of the options and best practices to allow you to create your end-to-end custom computer vision.
We break it down into six essential steps.
- Decide On the Collection Method
Third-party services or other internal resources are key in creating your own data set. For example, you could use automation or manual methods to collect data or a combination of both. Also, several online tools will help you with data scraping.
Manual methods will often call for a human element in the loop, which has to play by the rules and regulations of the business. Sensors or cameras are some of the devices that also come in handy in data collection.
One of the most famous examples of custom creation is autonomous vehicles. Some of these cars are self-driving and eco-friendly and will drive through towns and urban areas to gather vital data. The cars are often fitted with sensors and cameras to capture the needed visual data.
Remember to consider the best data annotation tool during the data collection process. This tool will significantly impact the success of the project and determine the workflow of the process.
- Group the Data in Tiers
Ensure that your data is grouped into smaller datasets to analyze its reliability better and develop an effective predictive model.
Break the more extensive sets into smaller, more manageable sets. For instance, if you target to work with 100,000 images, you could first group them in tiers of 5,000-10,000 and build upon them based on the model’s results.
Then, annotate the data monitoring its workflow every step of the way. Adjust when need be. Usually, it takes about four cycles to determine the tier that offers the most effective model performance.
Grouping the data in tiers is essential to minimize unwanted bias in the model. Working with larger tiers means that one unwanted bias could have you restarting the whole process.
- Data Validation
After data gathering comes to the validation, data validation aims to ensure all the quality metrics, including variance, density, and quantity, are met. At this stage, clean data of almost all unwanted biases before annotation can begin.
It might take a little bit of time, but this is nothing compared to what it would take to redo it if you miss the objectives the first time.
- Data Annotation
It is one of the most critical stages of the process and perhaps the most time-consuming. But, at earlier stages, you will have done some annotation, so this can be easy for you as well.
A reliable computer vision development service will handle the fine details for you. Or, you could learn to use various annotation techniques available as your choice. However, the most important factor is to make a thoughtful consideration to your decision as it will have a significant impact on the project’s success.
Your workforce options typically include:
- Contractors or freelance who work on-site or remotely
- Employees on payroll whose job description may not be included in the annotated data
- Business Process Outsourcing options not in the aces given to the workers
- Crowdsourcing platforms that allow you a larger pool of anonymous workers
- Model Validation
The quality of the developed algorithm should be validated. It is vital to determine if the data is an excellent fit for the algorithm.
This process requires humans in the loop to test the inferences presented by your model. It is an iterative step as sometimes changes are needed to the annotation process to give the best possible outcome. Adjustments to the algorithm might be needed at this stage.
Pro tip: using the tiered approach in gathering data will reduce the extra steps of scraping the model due to low-grade data.
It is not a one-day event. Machine learning is a repetitive activity to ensure all the models perform at the expected level. Therefore, be prepared to collect, annotate, and validate data again and again.
As the world changes, you might be required to retrain your machine to respond to the changes and the conditions.
Creating a custom end-to-end custom computer vision is a cyclical process that needs a well-thought approach. It requires a strategic interaction of technology, process, and the human factor. The more thoughtful the data collection and annotation process, the more likely you will have a successful project.