Wie testet und verbessert man Computer-Vision-Algorithmen?

Evaluation of metrics depends upon the problem. Like if you are doing object detection then use Intersection over Union IoU. For the classification accuracy, precision, recall, F1, confusion matrix are perfect. For all the problems, its also good to calculate the cost and time because these metrics are also good to get the good model which can help to reduce the resources requirements and time.

Finding a valid dataset poses a real challenge across different applications of computer vision. For some use cases of computer vision, the data can be taken from Kaggle or OpenImages databases, but tackling innovative real-world applications presents a distinct narrative. Here may need to collect data from live streams or employ synthetic or artificially generated data. In the process of data collection, it is important to ensure not only a good quantity but also a better quality of data because quantity can't only increase a model accuracy. After data collection, preprocessing techniques such as data augmentation and normalization can help to refine data for use in computer vision projects.

Also, it might be useful to refer to standard datasets as a publicly recognized benchmark. With all the major models assessing their performance on these data sets, this is the starting point for comparing me improving performance.

Using valid dataset is very important and also its very important that your training, validation and test data is also valid and covering all the scenarios. For example if you are doing cat and dog classification and you have most of the images dog in training set and less images in test set then its going to create overfitting and underfitting issues.

Once your baseline model is established, the next phase involves refining its performance. Begin by experimenting with various preprocessing techniques, such as image augmentation, normalization, and resizing, to enhance the input data quality. This can lead to improved edge and corner detection accuracy. Moreover, consider incorporating more complex architectures, like convolutional neural networks (CNNs) or feature pyramids, to elevate the baseline's capabilities. Regularly validate and fine-tune the model on your evaluation metrics. This iterative process might expose areas for enhancement, guiding subsequent enhancements.

Additionally, staying updated with the latest research and attending relevant workshops and conferences can provide insights into cutting-edge advancements. Regularly evaluating your algorithm's performance on diverse datasets and real-world scenarios is crucial. Ensemble methods, like model averaging, can further boost accuracy by combining the strengths of multiple models. Transfer learning is another powerful technique where pre-trained models are adapted for your specific task, saving time and resources. Incorporating data augmentation techniques, like rotation and flipping, can enhance model generalization. Lastly, keep an eye on ethical considerations to ensure fairness and privacy in your computer vision applications.