You have decided the problem you want to solve, NOW!?
You have decided the problem you want to solve, the Computer Vision Model you want to create, and the method you want to follow. The next step is to gather the data and start to label it.
You have heard that enlisting a 3rd Party Data Annotation Team or Company is good for your project. It helps eliminate your 3 main pains: Inaccuracy, Cost, and Slow-moving processes.
This seems simple enough, BUT, it might actually turn out to be rather tricky!
WHY? You might ask.
The answer is quite straightforward. The resultant annotated data might turn out to be highly inefficient and quite useless for your model actually. There are quite a few reasons why that might happen, but the main one is: You might not have gathered all the requirements for your project correctly and properly.
We still believe that enlisting the help of data annotation firms will provide you more benefit than completing the process in-house or via an automated tool. We’re just telling you how to crack the code and obtain the highest efficiency and accuracy for your dataset.
This article guides you through the process of “How-To: Gather all the requirements for your data annotation project the right way.”
Step 1: Know your end goal very clearly
Remember to explain the Computer Vision Model you are creating and the solution you are trying to achieve very clearly to the customer support/ operations team in the Data Annotation company.
Discuss the nature of the dataset that you’ll be providing thoroughly. You can talk about volume, technical expertise needed, source of the dataset, and more
Try to provide a sample of the use case that you are looking for, especially if it is complex or unique. This helps both teams visualise what needs to be done.
Step 2: Document even the smallest details
Once you have explained the use case that you looking for, invest some time to write down all the technical requirements that you need.
We suggest you spend a short while with the customer success team to create a written guide that both of you can keep referring to.
The key is in the details. The more written details you provide, the more certain you can be that the resultant annotated dataset will correctly fulfil all your requirements.
Be frank about what you need!
Step 3: Start small (Experiment with small batches of data)
Once you have completed the first 2 steps, it is now time for the action to start.
Section out a very small dataset and give it to the Annotation Company to tag/label. This will allow:
You to understand their work process, the accuracy they achieve, and the benefits you can get.
Them to understand your data better and figure out the best way to use their annotation resources.
Start small. Find your bearing. Once you can be sure the small-batch is perfect, iteratively grow the data size, and strengthen your requirements.
Step 4: Communicate!!!
Find an annotation company that will invest in the time to hear you out, even between the project. (Tictag has an excellent CRM model)
The more you communicate, the more you can be sure they understand your data and requirements.
But, remember not to make major changes in the middle of a project, it might lead to you having to restart the entire project from scratch!
Step 5: Don’t be shy. ASK QUESTIONS!
Ask to know their process
Ask to understand their resources
Ask to identity the timeline that suits you both
Ask for the in-between information you need
Ask if something might be going wrong with the data
Don’t assume, Just Ask!
Remember, it is the successful partnership between yourself and the data annotation company that can result in good data and an even better Computer Vision Model.
Remember to select the right Data Annotation Company that pays attention to YOUR needs.
Tictag is a data annotation company based in Singapore that has one of the most excellent CRM models in the industry. It has made it a point to invest as much time in the customer, as it does in the project.
Tictag is young and adventurous when it comes to taking on unique and complex datasets. But, at the same time, it is highly reliable and will give you results with an accuracy of 99.5%+ in record time.