How to train a good model with deep learning

Deep learning has been widely used in recent years, and has achieved outstanding performance in fields such as image recognition, speech recognition, and natural language processing. However, it is not easy to train an efficient and accurate deep learning model. Not only high-quality data, suitable models and sufficient computing resources are required, but also reasonable hyperparameter adjustment, data enhancement and model fine-tuning are required according to the characteristics of the task and data. In this article, we will introduce the training process of the deep learning model in detail, discuss hyperparameter settings, data enhancement techniques, and model fine-tuning to help readers better train efficient and accurate deep learning models.

This article will discuss data, models, hyperparameters, and training techniques

data

From the data level, there are two factors that can affect the performance of the model:

1. Dataset Quality
2. data augmentation

dataset quality

Data Quality: Data should be accurate, complete, error-free, and representative. If the data set is wrong or missing, it will affect the performance of the model. The higher the resolution, the better the model is, but it is also necessary to consider whether the memory occupied by the model training is enough, because the higher the resolution, the larger the amount of data. bigger

Amount of data: More data generally improves model performance because it makes the model more representative and generalizable. However, the size of the dataset also affects training time and resource requirements. But for the convergence of model training, the amount of data has no decisive impact on the convergence of the model. It can only be said that the larger the amount of data, and the better the distribution of data diversity, the model will definitely generalize

Data Diversity: For better generalization, the dataset should be diverse. This means that different samples should be included so that the model can learn various patterns in the data. For sample diversity, the number of samples in each category should be the same, it is better to add negative samples (positive samples are image annotation information, negative samples have no annotation information, for example, someone has a car in the positive sample image , there is nothing in the negative sample image). Among them, the ratio of positive samples to negative samples is recommended to be 1:2 or 1:3. This is because there are more negative samples than positive samples in the real world, but it should also be judged according to the scene of your own model. How many negative samples, The model will be biased towards identifying negative samples and will not be able to identify positive samples.

Data preprocessing: Before selecting a data set, it is necessary to understand the characteristics of the data and perform preprocessing. For example, for an image classification problem, it may be necessary to scale or crop the image, or normalize pixel values ​​to the [0,1] range.

Data sources: Reliable data sources should be selected. Some datasets may come from unreliable or inauthentic sources, which may lead to poor performance of the model.

Data Splitting: While choosing a dataset, the data should be split into training set, validation set and test set. This can be used to evaluate the generalization ability and performance of the model.

Data labeling: In some tasks, data needs to be labeled in order to train the model. This may require a lot of manual labor and time costs. However, it should also be noted that although the number of different categories in the dataset reaches the same balance, if the number of one category marked in the image is much larger than the number marked in the image of another category, it will also lead to data imbalance . Therefore, some method adjustments are required, as follows:

1. Oversampling: For samples of minority categories, the number of samples can be increased by copying, interpolation, etc., so that the number of samples of different categories is more balanced.
2. Undersampling: For samples of most categories, some samples can be randomly deleted, so that the number of samples of different categories is more balanced.
3. Weighting: For samples of different categories, different weights can be assigned to each sample, so that the model can pay more attention to samples of minority categories. In general, weights can be obtained by computing the inverse of the sample proportion for each class.

For example, suppose we have a binary classification task where the proportion of samples from the minority class is 0.1 and the proportion of samples from the majority class is 0.9. Then we can assign a weight of 1/0.1=10 to the samples of the minority category, and a weight of 1/0.9=1.11 to the samples of the majority category, so that the model pays more attention to the samples of the minority category.

In implementation, it is generally possible to adjust the sample weight by setting weight parameters of different categories in the loss function, or using some loss functions for unbalanced data (such as Focal Loss).

Summarizing the above information, it can be found that for data to affect model performance, it mainly revolves around the image quality of data and the balance of data (including data size, ratio, and labeled data volume)

data augmentation

In deep learning, data augmentation is a very important technique, which can expand the size of the data set, improve the generalization ability of the model, and alleviate the problem of overfitting. The following are some common data enhancement methods, and also explain which data enhancement methods are suitable for which scenarios.

In addition to converting structured data into tensors, other methods of data enhancement are not used casually, and must be used in conjunction with appropriate scenarios.

Some commonly used data augmentation methods are listed below:

Random cropping: Randomly select an area in the image for cropping, so as to obtain multiple different cropping results.

Random flipping: Randomly flip the image horizontally or vertically to get different mirror results.

Random rotation: Randomly rotate the image to obtain images with different rotation angles and directions.

Random scaling (Random scaling): randomly scales the image to obtain images of different sizes.

Random color jitter: Perform random color jitter on the image, such as adjusting brightness, contrast, saturation, etc.

Add noise: Add random noise to the image to make the model more robust.

In practice, the appropriate data augmentation method is usually selected according to the characteristics of specific tasks and datasets. Among them, random cropping, random flipping, and random rotation are common methods in computer vision tasks. It is not difficult to imagine why people recognize things in real life, even if things have been rotated, only part of them

It is also necessary to consider the actual scene and choose the appropriate method. You have to think more about the specific situation, such as

• There is no possibility of rotation of things in a scene, and there is no need to enhance the rotation of the data.
• If the scene is exposed in an open place, the influence of light should be considered, and the color of the data needs to be enhanced.

At the same time, when using data augmentation methods, care needs to be taken to avoid over-augmenting the data, otherwise it will negatively affect the performance of the model. In addition, in order to avoid overfitting, the generalization ability of the model can also be improved by using different data augmentation strategies for different data sets.

model selection

Choosing a computer vision model that suits you requires consideration of multiple factors, including task type, dataset, model complexity, and computing resources.

First of all, you need to clarify your task type is image classification, target detection, semantic segmentation, instance segmentation, pose estimation, face recognition, video analysis, etc. Different types of tasks require different models.

Secondly, the data set used needs to be considered. The scale, characteristics and difficulty of the data set will affect the performance and selection of the model. For example, for smaller datasets, lightweight models can be used, while for complex datasets, more complex models such as deep residual networks, attention mechanisms, and Transformers are required.

In addition, the limitations of computing resources, such as computing power, memory size, and video memory size, need to be considered. If computing resources are limited, you can choose some lightweight models or use techniques such as distributed training to speed up training.

Finally, the complexity and training difficulty of the model also needs to be considered. Generally speaking, the more complex the model, the more computing resources it needs and the more difficult it is to train. Therefore, it is necessary to balance model complexity and performance when choosing a model.

In addition to the above factors, there are other factors that need to be considered, such as:

1. Accuracy: The accuracy of the model is one of the important indicators to measure the quality of the model. In practical applications, you need to choose the model with the highest accuracy according to your task requirements.
2. Interpretability: Some tasks require the model to provide interpretable results. For example, in target detection, it is necessary to know the object category, position, and size corresponding to each detection frame. Therefore, interpretability needs to be considered when choosing a model.
3. Real-time: Some applications require the model to respond in real time, such as unmanned driving and robot control. Therefore, its response time and efficiency need to be considered when choosing a model.
4. Data augmentation: Data augmentation is a commonly used technique to improve model performance. It can reduce the overfitting problem of the model by expanding the data set. Therefore, the degree of support for data augmentation needs to be considered when choosing a model.
5. Transferability: Some applications require the model to be able to migrate across different scenarios and tasks, such as using pre-trained models for fine-tuning. Therefore, its transferability needs to be considered when choosing a model.
6. Scalability: Some applications require models to be able to run on different devices and platforms, such as embedded devices and mobile devices. Therefore, scalability needs to be considered when choosing a model.

To sum up, choosing a computer vision model that suits you needs to consider many factors, and you need to choose according to specific application scenarios and task requirements. At the same time, it is also necessary to pay attention to the latest research progress and algorithms in order to better cope with the ever-changing computer vision tasks and application requirements

For specific model selection, the editor thinks that we can first consider filtering out inappropriate models based on the complexity, real-time performance, and accuracy of the model, and then start with a model that is complex and small, and use its pre-trained model for training. Factors such as loss and convergence after training are used to determine whether to choose a more complex model

hyperparameters

In deep learning, hyperparameters refer to those parameters that need to be set manually. These parameters cannot be learned directly from the data, but need to be adjusted and optimized to obtain the optimal model. The choice of hyperparameters has a strong impact on the training and generalization performance of the model.

The following are common hyperparameters and what they do:

• Learning rate (learning rate) : The learning rate controls the speed of parameter update. Too small learning rate will lead to slow training speed, while too large learning rate may lead to unstable training or even failure to converge. It usually needs to be adjusted according to the specific problem and network structure.
• Batch size (batch size) : The batch size refers to the number of samples used in each iteration. A too small batch size will increase the training time, while a too large batch size will take up too much memory. Often adjustments are required at the beginning of training.
• Number of epochs (number of iterations) : The number of iterations refers to the number of rounds of training. Too few iterations will lead to underfitting, while too many iterations will lead to overfitting. It usually needs to be determined according to the performance of the training set and the validation set.
• Dropout rate : The dropout rate refers to randomly discarding a certain proportion of neurons during training to prevent overfitting. A dropout rate that is too high can lead to underfitting of the model, while a dropout rate that is too low can lead to overfitting. It usually needs to be adjusted according to the specific problem and network structure.
• Regularization : Regularization prevents overfitting by punishing model complexity. Common regularization methods include L1 regularization and L2 regularization. Need to be adjusted according to the specific problem.
• Optimizer : The optimizer controls how the model parameters are updated. Common optimizers include SGD, Adam, and RMSprop. Different optimizers may have different effects on different problems and network structures.

In deep learning training, hyperparameters refer to parameters that need to be manually set during training, such as learning rate, batch size, regularization coefficient, etc. Different values ​​of hyperparameters will have different effects on the performance of the model, so reasonable settings are required.

If the hyperparameters are too large, it may cause the model to overfit, that is, perform well on the training set, but perform poorly on the test set or new data; if the hyperparameters are too small, it may cause the model to underfit, that is, the model It performs poorly on both the training set and the test set. Therefore, it needs to be adjusted according to the dataset and model structure.

Generally speaking, when setting hyperparameters, you need to use default values ​​or empirical values ​​as a starting point, and then adjust and verify step by step. Typically, the learning rate can be set to 0.001 or 0.0001; the batch size can be set to 32 or 64; the regularization coefficient can be set to 0.01 or 0.001, etc. These values ​​can also be fine-tuned for specific tasks and datasets.

In addition, there are some more advanced hyperparameter setting methods, such as grid search, random search, Bayesian optimization, etc.

skills in training

Because the cost of training a deep learning model is higher, it is impossible to use multiple hyperparameter combinations to train the model and find the optimal model. How to train a good model at a low cost?

In the case of low cost, the following methods can be used to train a good model:

1. Early Stopping : While training a model, we can track performance on the validation set and stop training when performance is no longer improving. This prevents the model from overfitting and saves training time.
2. Random search hyperparameters : Hyperparameters are configuration options for the model, such as number of layers, number of nodes, learning rate, etc. Random search hyperparameters can help us find the optimal model without trying all possible hyperparameter combinations.
3. Use a pre-trained model : A pre-trained model is a model trained on a large dataset, which can be used as an initial model to speed up the training process and improve model performance.
4. Transfer learning : Transfer learning refers to applying a pre-trained model to a new task and then fine-tuning it to fit the new task. This can help us train better models on small datasets.
5. Batch regularization technology : Batch regularization technology, such as batch normalization (Batch Normalization) and weight decay (Weight Decay), can help us train a more stable and accurate model.
6. Hardware optimization : Using better hardware, such as GPU and TPU, can help us speed up model training and save time and cost.
7. Comparative experiments : Conducting comparative experiments is also a way to select the optimal model. Comparative experiments refer to training and testing different models under the same data set and task, and comparing their performance through some evaluation indicators. You can choose some commonly used models as baselines, such as ResNet, Inception, VGG, etc., and then try some new models, such as EfficientNet, RegNet, Vision Transformer, etc., and train and test them under the same data set and task. Compare their performance differences to find the optimal model. It should be noted that the comparison experiment needs to select appropriate evaluation indicators, such as accuracy rate, F1 value, mean average precision (mAP), etc. At the same time, factors such as training time, model size, and inference speed also need to be considered. Therefore, a more accurate conclusion can only be drawn by comprehensive consideration of multiple aspects.
8. Integrated learning : refers to the method of combining the prediction results of multiple models to obtain more accurate prediction results. Common ensemble learning methods include voting, averaging, stacking, etc. Voting refers to voting on the prediction results of multiple models, and selecting the result with the most votes as the final prediction result. The average means that the prediction results of multiple models are averaged as the final prediction result. Stacking refers to taking the prediction results of multiple models as input and training a new model to get the final prediction result. It should be noted that ensemble learning needs to select multiple models with similar performance to combine, otherwise the prediction performance may be reduced. At the same time, integrated learning also needs to consider factors such as model training time and model size.

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