by Shankar Jagadeesan
LandingLens empowers you to deploy accurate computer vision models quickly. With the LandingLens optimized deep-learning modeling architecture and intuitive interface, you can create and deploy a computer vision project without needing a background in machine learning.
Typical Computer Vision Process
The typical process for creating a computer vision model includes these steps:
- Collect images.
- Mark the “ground truth” areas of the images. When you train your model, it will learn to detect these areas.
- Train the model.
- Deploy the trained model on an edge device to perform inference.
This whole process is iterative. For example, after you train a model and see the results, you might want to add more images to the dataset or make the ground truth annotations more precise.
Is Model Training a Bottleneck?
When creating a computer vision model, the training step can become a bottleneck. This step usually involves iterating on your dataset and can be complex if you’re working with a high-code application or don’t have a background in machine learning.
Iterating quickly has many advantages:
- You have a higher chance of developing a good model if you complete all training, debugging, and error analysis in one session. If you have long gaps between training sessions, you might accidentally leave out corner cases or skip analyzing errors.
- For certain domains, like manufacturing, a problem in the QA process pipeline is sometimes not identified until a later stage, which means there is not enough time to implement a fix.
- If you’re using a computer vision program that requires a thorough understanding of deep learning, but you’re not a machine learning engineer, your model could initially yield poor results. This could get you stuck in the modeling phase. Slow iteration leads to slow feedback, which in turn slows down the whole process.
Speed Up Model Training with LandingLens
To avoid the pitfalls that lead to a long and slow training process, use LandingLens to create and train your computer vision model.
The no-code interface guides you through the full process, and doesn’t require you to have any background knowledge of artificial intelligence, deep learning, or computer vision. After you upload images, mark the ground truth with bounding boxes. Then, simply click Train to train your model!
The computer vision architecture in LandingLens has been fine-tuned and optimized for most scenarios, so you don’t need to worry about configuring any settings.
You can immediately see the results of the model training, and iterate on your dataset if needed.