YOLOv5 Hyperparameters, Explained.
Object detection has become an essential task in computer vision, with a wide range of applications from self-driving cars to video surveillance. One of the most popular and efficient approaches for object detection is the You Only Look Once version 5 (YOLOv5) neural network architecture.
However, the YOLOv5 neural network architecture has several hyperparameters that can significantly affect its performance. These hyperparameters control the model’s behavior during training and inference, and selecting appropriate values for them is crucial for achieving optimal results. Unfortunately, the original code for YOLOv5 is not well-documented, making it challenging for researchers and practitioners to understand and modify the system.
Even though I work with this tool daily, I’m not the best writer and sometimes struggle with explaining technical concepts. With the assistance of ChatGPT, a state-of-the-art language model, I will try to explain the major hyperparameter’s purposes, their impact on the network’s output, and how to fine-tune them for different object detection tasks.
YOLOv5 Hyperparameters:
lr0, lrf — The learning rate is a hyperparameter that determines the step size at which a neural network’s parameters are updated during training. The choice of a learning rate is critical in determining how quickly the network converges to the optimal solution and how well it generalizes to new data.
Reducing the learning rate during training is a common practice to improve convergence and generalization in deep learning neural networks. Large initial learning rates (lr0) allow the system to approach the optimal solution more quickly, but can cause the network to overshoot, leading to unstable and oscillatory behavior that prevents the network from converging to a good solution. Therefore, as the network approaches the optimal solution, the gradients become smaller, inhibiting the network from overshooting the minimum and bouncing around it without converging.
Later in training, a smaller learning rate helps the network explore the parameter space more finely and find a better minimum that generalizes well to new data. The lrf parameter controls how small the learning rate will be at the end of training.
momentum — The momentum effect helps the optimizer to move more efficiently through narrow valleys and noisy gradients in the loss landscape, enabling it to converge faster to the global minimum of the loss function.
The momentum term also helps to reduce the effect of noise in the gradient estimates, which can lead to more stable optimization and better generalization. For the Adam optimizer, the typical momentum values are between 0.9 and 0.999.
weight_decay — Weight decay is a regularization technique used in deep learning to prevent overfitting by adding a penalty term to the loss function. The penalty term is proportional to the square of the magnitude of the weights in the network. The weight decay hyperparameter controls the strength of the penalty term and determines how much the weights are shrunk towards zero.
In the context of object detection, weight decay can affect the performance of the network by influencing the balance between underfitting and overfitting. When weight decay is set too low, the model may overfit the training data, resulting in poor generalization performance on new data. On the other hand, when weight decay is set too high, the model may underfit the training data, resulting in poor performance on both the training and test data.
Furthermore, weight decay can also affect the speed of convergence and the final accuracy of the model. If weight decay is set too high, the model may converge more quickly but may not reach its full potential accuracy. On the other hand, if weight decay is set too low, the model may take longer to converge but may eventually reach a higher level of accuracy.
cls_pw, obj_pw — In object detection, the binary cross-entropy loss is commonly used to train the network to distinguish between object and non-object regions in the input image. The loss is computed based on the predicted probability of each pixel or anchor box belonging to an object or not, and the ground truth labels.
Typical values for the binary cross-entropy loss weight in object detection networks vary depending on the specific application and dataset. Values between 1 and 10 are commonly used, but values outside of this range may also be appropriate depending on the specifics of the problem being solved.
iou_t — Intersection over Union (IoU) threshold is used to determine whether a predicted bounding box for an object is considered a true positive or a false positive. The IoU threshold is the minimum overlap required between the predicted bounding box and the ground truth bounding box in order for the prediction to be considered a true positive.
A high IoU threshold will result in fewer but more accurate detections, while a lower threshold will result in more detections but with a higher false positive rate.
anchor_t — anchor boxes are predefined bounding boxes of different sizes and aspect ratios that are placed at different locations on an image grid. The anchor_t parameter, also known as the anchor-multiple threshold, is a hyperparameter that determines the maximum adjustment that can be made to the anchor boxes during training.
During training, the neural network learns to adjust the anchor boxes to better match the objects in the input image. However, if the adjustment is too large, the anchor box might end up not matching any object in the image or matching multiple objects, leading to incorrect detections. Therefore, the anchor_t parameter limits the maximum adjustment that can be made to the anchor boxes.
More specifically, the anchor_t parameter determines the maximum multiple of the original anchor box size that the adjusted anchor box can have. For example, if the anchor_t parameter is set to 2, the adjusted anchor box can be at most twice the size of the original anchor box.
fl_gamma — Focal Loss is a modification to the cross-entropy loss function, which aims to address the problem of class imbalance in object detection tasks. In the standard cross-entropy loss, all classes are weighted equally, which can lead to poor performance when there are many easy-to-classify examples and only a few difficult ones. Focal loss assigns a lower weight to easy examples and a higher weight to hard examples, thereby allowing the model to focus more on the difficult examples.
The value of gamma depends on the specific task and the dataset. Generally, a value between 1 and 3 is commonly used. A larger value of gamma means that the network will focus more on the hard examples, while a smaller value means that the network will be more balanced in its treatment of easy and hard examples.
Hopefully this helps explain some of the more obscure parameters and lets you improve your models! Let me know if you have any comments or notice any inconsistencies.