To optimize your model for the best possible outcome, keep the following points in mind. These tips can serve as valuable tools in some situations, while potentially causing harm in others. Choose your strategies carefully.
Balance the classes by ensuring each class has an equal number of images. For instance, when creating an image classifier to distinguish between apples and bananas, the dataset should contain an equal number of apple and banana photos. This applies to object detection models as well, where the quantity of banana and apple labels should be balanced.
Data augmentation techniques, such as cropping, scaling, flipping, rotating, translating, adding noise, and modifying lighting conditions, typically yield better results compared to non-augmented data.
Adjust image size to 256x256 pixels and fill any empty spaces with black for optimal results in UseAI. This is the format images are converted to when users take photos with UseAI.
Determine the best sensitivity using a confusion matrix. In some cases, adjusting the sensitivity can enhance model performance, particularly in object detection models. For example, setting the sensitivity below 50% may result in higher recall and precision.
Inspect the dataset for bias. For example, in the ISIC-2020 Challenge, the dataset contained images of malignant and benign birthmarks, with pen marks often present in malignant samples. By removing pen marks before training, you can create a better-performing, unbiased model.
Add more data to your model, as this can improve performance in 99.9% of cases.
Incorporate additional layers into your model to help it recognize more features from the data. This is particularly important for complex tasks, such as distinguishing between very similar objects or images.
Increase the number of epochs up to the point where the model achieves maximum accuracy and reaches its global minimum.
Reduce the color channels if color is not crucial for your model. For instance, when creating an image classifier for numbers, the color of the numbers should not impact the results. This can also lead to a smaller and faster model.
Add synthetic data to your dataset. Although this field is relatively new and has some drawbacks, it generally has a positive impact on model performance.
Clean the dataset and remove duplicate images, as they can lead to poorer performance.
Include unlabeled photos in your dataset when training an object detection model. This can help reduce false-positive results. For example, when creating an object detection model for fruits, adding photos that could cause confusion, such as a pokeball, can help the model learn that it is not an apple.