I need help with automated screenshot validation using ML
I’m managing a fullscreen app that runs on many machines at once. The problem is that sometimes it crashes and users see the desktop or random popup windows instead of the proper application interface.
Right now I have collected a bunch of screenshots - thousands showing the app working correctly and several hundred showing when things go wrong. I want to build something that can automatically tell the difference between good and bad screenshots.
Is there any machine learning solution or ready-made API that I can train with my good and bad examples? I’m looking for something that can learn from these labeled screenshots and then predict whether new screenshots show a healthy application or a crashed state.
Any suggestions for frameworks or services that handle this kind of image classification task would be really helpful.
Been there with monitoring systems across our distributed fleet. Skip the heavy ML training if you want something running fast.
AWS Rekognition Custom Labels handles this perfectly. Upload your good/bad screenshot folders and it builds the classifier. No GPU setup, no model decisions. Takes 30 minutes versus days with TensorFlow.
One trick that saved us: create a simple hash comparison as backup. Sometimes “failure” is just the app stuck on a loading screen that looks identical every time. Kept us from overcomplicating things.
For scale monitoring, grab multiple screen areas, not full desktop shots. Focus on the app’s status bar or specific UI elements that show when healthy. Way more reliable than analyzing entire screenshots.
Google AutoML Vision works great if you’re not on AWS. Both beat building from scratch unless you have very specific needs they can’t handle.
honestly, just use scikit-learn for this. way simpler than tensorflow. extract basic image features like color histograms and edge patterns, then run them through a random forest classifier. worked great for our monitoring setup and trains in minutes, not hours. much easier to debug when things go wrong compared to deep learning black boxes.
OpenCV combined with a lightweight CNN proved effective for our remote desktop monitoring. Instead of relying solely on image classification, we implemented template matching first to extract key UI elements that should consistently be present when the application runs correctly, such as toolbars, status bars, and specific buttons. If template matching fails to locate these elements, we can immediately flag the screenshot without needing the ML model. For the machine learning aspect, we found that using PyTorch with a simple ConvNet outperformed transfer learning given the specific patterns present in our screenshots. A crucial insight we gained was to augment our training data with various display scaling and color variations, as machines often have highly diverse monitor setups. Additionally, when it comes to confidence thresholds, we were cautious; we discovered that confidence levels between 70-80% often indicated a new failure mode. Instead of risking inaccurate classifications, we opted to flag those cases for manual review and included them back into our training data. This significantly reduced our false positives compared to rigid binary classification methods.
Had the same problem with our kiosk deployment about two years back. TensorFlow’s image classification with transfer learning saved me tons of time. Grab a pre-trained model like MobileNet or ResNet and just retrain the final layers on your screenshot data. Way less training data needed and doesn’t eat up resources like training from scratch. Biggest lesson learned: your training data needs every failure type you can think of. Don’t just focus on crashes - include network timeouts, auth prompts, system dialogs, the works. Also helped to crop screenshots down to the specific UI elements that show when the app’s running properly. Accuracy jumped when I mixed in screenshots from different times of day and screen resolutions. Took 4-6 hours on a decent GPU and hit 94% accuracy on our test set.