Como você pode criar sistemas de visão computacional escaláveis e robustos?

Having good data is like using a strong engine for a computer vision algorithm. Without good data, the algorithm won't work well. 🔍 Many times, we only collect data and directly use it for the algorithm after data annotation but it's not always the case, Sometimes the video streams do not provide good data, or they have a lot of noise or blur pictures. If you use this data directly, the algorithm might not work right. You may be required to make the data better, remove extra noise, or adjust the size of pictures. 📖

To create scalable and robust computer vision systems, several vital elements must be considered. Firstly, the data quality and quantity must be sufficiently diverse to train the model. Secondly, the model architecture and training process must be optimized for performance and accuracy. Thirdly, the algorithm design and implementation should be able to handle different scenarios and challenges. Fourthly, the infrastructure and deployment should be able to handle the workload and support the system. Minimizing hardware footprint and optimizing inter-process communication mechanisms are essential to achieve scalability. Minimizing latency, deployment risks, and distributed transactions is necessary for robustness.

Choosing the right computer vision algorithm isn't easy at first. It requires lots of experiments, trying different things, and finding what works best. Here are a few tips for picking an algorithm. ⭐ If you are deploying a model on embedded devices use a smaller version of the algorithm for speed. However, training might take many tries and iterations to be accurate. 🔍 Sometimes, the problem you're solving hints at the best algorithm, but for this you must have strong experience with different algorithms first. ⭐ Testing the algorithm on validation data isn't enough real-world performance matters more than validation data. Scores might look good If your validation data have common features as you used in training data.

Creating scalable and robust computer vision systems involves careful consideration of algorithm design and implementation. The algorithm, responsible for processing visual data and generating outputs, must be versatile enough to handle diverse scenarios and challenges, including occlusion, illumination, motion, and perspective, while also addressing noise, ambiguity, and uncertainty. To achieve this, it is crucial to develop algorithms with clear, modular code that adheres to coding standards and best practices. Additionally, thorough testing and debugging using tools like unit testing, logging, profiling, and debugging help ensure the algorithm's reliability and effectiveness across various cases.

Computer vision project deployment is a big and important step. There are a few ways to do this. You can use services like GCloud, AWS server, or Microsoft Azure. 🔍 If you put your solution on the cloud, it will be quick, easy, and work well. But sometimes, it might cost a lot, because you will need to pay for cloud service every hour of its utilization. 🔍 If you use small computers like Jetson Nano, things are a bit trickier. You need to make sure your model runs fast and smooth. This can be a bit hard, but it doesn't need expensive hardware and this way is much more cost-effective.