How do Hyperparameters of YOLOv5 Work?
YOLO-v5 is a modern object detection algorithm, that has been written in a PyTorch, Besides this, it has, fast speed, high accuracy, easy to install and use.
The importance of yolov5 was raised, due to its different export and deployment modules. We can convert the trained model (.pt) in many extensions i.e.,
- torch(.pt) → tensorflow-lite(.tflite) {for android development, etc)
- torch → open-neural-network-exchange (.onnx) {for cross platforms}
- torch → Tensorrt-engine (.trt/.engine) {for Tensorrt integration)
What are Hyperparameters?
The type of parameters whose values control the whole learning process of the deep learning model. In addition, it also determines the values of model parameters that a learning algorithm ends up learning, While it is not part of the deep learning model.
YOLOv5 Hyperparameters?
YOLOv5 has about thirty(30) different hyperparameters used for different training settings. If you want to achieve better results, You can optimize them. if your parameters optimization is better, you will get better results. If you are not very familiar with these, you can use their default values
that are used in training on the COCO dataset.
