Triage The report describes the YOLO5 algorithm that underpins the approach in question and can be classified as machine learning for object detection and classification conducted to identify different dog breeds. The project entailed curating a huge number of images of dogs, creating a bounding box for each of them similarly to the previous project, and marking the breed of the dog in the image, training the YOLO5 model, releasing the model and using it to achieve real-time detection of the breed of a dog. The report covers the issues encountered during the process, including dealing with class imbalance, the management of computational resources, and data quality assurance. It stresses the significance of appropriate data pre-processing, annotation, and evaluation measures. It also provides and analyzes the results of training the YOLO5 model such as confusion matrix, F1 curve, labels distribution, labels correlogram, precision-recall curves, batch images. The report shows the real-world problem using the YOLO5 algorithm for object detection and classification for pets’ identification, animal monitoring, and behavior analysis. It also presents some of the improvement strategies and the future research areas for improvement. On the whole, the given report may be deemed helpful for the comprehension of the problem of applying advanced object detection algorithms, with the focus on data quality, model evaluation, and incremental approach.
