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.
Below, I have discussed some useful parameters, that can boost your results.
- Learning-rate start (lr0): learning rate starts to determine step size at each iteration. I.e. if you configure the learning rate (0.1) before training, then it means at every iteration, your training progress will increase by 0.1.
- Learning-rate end (lr1): learning rate end, used to check the following mentioned condition,
If (current learning rate>=learning-rate-end):
Stop training
else
Continue training
- Momentum: Momentum, is the tuning parameter for the gradient descent algorithm, its work is to replace the gradient with an aggregate of gradient
- Mosaic: Mosaic, is used to increase model accuracy by creating a new image from a combination of multiple images, and then using newly created images for training. It is well known for data augmentation and finding optimal feature techniques.
- Degree: Degree, is used to increase model accuracy by randomly rotating images up to 360 degrees in whole training data. This parameter helps most when you need to detect objects in multiple positions/angles.
- Scaling: Scaling, is used to resize an image either to match with grid size or for optimization of results.
- Flipud: Flipud, is used to flip images up and down randomly from the complete dataset to achieve better results. This can be considered in the data augmentation technique.
- Fliplr: Fliplr, is used to flip images left and right randomly from the complete dataset for achieving better results. This can be considered in the data augmentation technique.
- weight-decay: A type of regularization technique, that works by adding a penalty term to the cost function of a network, which has the effect of shrinking/compressing the weights during the backpropagation process.
There are many other parameters, including warmup epochs, warmup momentum, etc., whose values can be changed according to specific use cases.
That’s all regarding “How do Hyperparameters of YOLOv5 Work? For more details, you can visit YOLOv5-Hyperparameters Evolution
About Me
- Muhammad Rizwan Munawar is a highly experienced professional with more than three years of work experience in Computer Vision and Software Development. He is working as a Computer Vision Engineer and has knowledge and expertise in different computer vision techniques including Object Detection, Object Tracking, Pose Estimation, Object Segmentation, Segment Anything, Python, and Software Development, Embedded Systems, Nvidia Embedded Devices. In his free time, he likes to play online games and enjoys his time sharing knowledge with the community through writing articles on Medium.
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