Each of your models automatically goes through testing on an evaluation dataset during the training phase. You can also add a separate test set.
You can test your models both via standard Rest API endpoint and in Ximilar App. Click on Tasks, scroll down to Models and click on a magnifying glass to view the details about a particular model. There you can check the evaluation metrics (accuracy, precision, recall, confusion matrix, and failed images). Based on these metrics, you can further iterate and improve your training dataset to make your model more robust. We are also able to change the architecture of the neural network if needed and improve the robustness for your specific data.
You can also test the accuracy of your model on a particular image. Drag & drop the image or copy its URL to Test under your service. Go to: Evaluation on an Independent Dataset Guide: Inspect the Results and Errors Guide: Reliability of the Image Recognition Results