Computer Vision with Machine Learning: 7 Common Problems & Solutions

Machine learning is a complex process, and it’s no wonder that many people encounter problems while trying to build a model. In this blog post, we will discuss the most common issues that people face, and how to deal with them. By understanding these problems and knowing how to address them, you will be able to create successful machine learning models!

The following are 7 problems and their solutions that I have gathered for you:

1. Lack of data

Lack of data is one of the most common problems that people face when trying to build a machine learning model. To create a model, you need data! If you don’t have enough data, you won’t be able to train your model properly, and it will not perform well.

One way to get around this problem is to gather more data. This can be done by collecting data from different sources or augmenting your existing data set. Augmenting your data set means adding more information to it so that it is larger and contains more variation.

Another way to deal with the lack of data problem is to use pre-trained models. Pre-trained models are models that have been trained on large datasets, so they already have a lot of information. You can use these models as a starting point and customize them to fit your data.

👇Just for this type of problem we created images.cv:

images.cv | Image datasets for computer vision and machine learning

2. Overfitting

Another common problem is overfitting. Overfitting occurs when the model learns too much from the training data and starts to perform poorly when applied to new data sets. This can happen when the model is too complex or lacks enough data to properly train it. To avoid overfitting, you can try to use a simpler model, or increase the size of your data set.

Regularization: This technique involves adding extra constraints to the model to prevent it from overfitting.

Cross-validation: This technique splits the data into different parts, then tests the model on each part. By doing this, you can determine how well the model is performing on different data sets.

Data augmentation: This technique involves adding more variation to the data to make it harder for the model to overfit.

Model selection: This technique involves choosing a model that is less likely to overfit the data.

Learn more on Overfitting, in this great Medium article by Farhad Malik

The Problem Of Overfitting And How To Resolve It

3. Bias in the data

Bias in the data is another common problem that people face when trying to build a machine learning model. Bias means that the data is not representative of the real world. This can cause your model to perform poorly when used in the real world.

There are several ways to deal with bias in the data:

1. One way is to randomly select a different subset of data for each training iteration. This will help ensure that your model is not biased towards any specific set of data.
2. Another way to deal with bias is to use cross-validation. Cross-validation splits your data into several smaller sets and then trains your model on one set of data and tests it on another set of data. This helps ensure that your model is not biased towards any specific data set.
3. Finally, you can also use regularization to reduce the amount of bias in your model. Regularization is a technique that helps prevent overfitting by adding extra constraints to the model. This makes the model more accurate, but it also reduces its flexibility.

Learn more on machine learning bias in the data, in this great Medium article by Sara A. Metwalli

5 Types of Machine Learning Bias Every Data Scientist Should Know

4. Noise

Noise in the data is another common problem people face when building machine learning models. Noise means that the data is not clean and contains irrelevant information. This can cause your model to perform poorly when used in the real world.

There are several ways to deal with noise in the data:

1. One way is to remove noisy values from the data set. You can do this by using feature selection or dimensionality reduction techniques. These techniques help you identify and remove the noisy values from your data set.
2. Another way to deal with noise is to use pre-trained models. Pre-trained models are already trained on clean data, so they are less likely to be affected by noise.
3. Finally, you can also add more training data to help reduce the amount of noise in the model. This will help make the model more accurate.

You can read more about how to deal with noise in data, in this interesting Medium article by sunil Belde

Noise removal in images using deep learning models

5. Model instability

Model instability is another common problem people face when building machine learning models. Model instability means that the model keeps changing its predictions as it trains. This can cause the model to perform poorly when used in the real world.

There are several ways to deal with model instability:

1. One way is to use a better optimizer. The optimizer is the algorithm that is used to train the model. There are several different optimizers available, and each one has its strengths and weaknesses. You can try using a different optimizer to see if it helps stabilize your model.
2. Another way to deal with model instability is to use more data. More data will help the model learn more about the problem, which will make it less likely to change its predictions as it trains.
3. Finally, you can also use cross-validation to help stabilize your model. Cross-validation splits your data into several smaller sets and then trains your model on one set of data and tests it on another set of data. This helps ensure that the model is stable across different datasets.

You can read more about how to deal with model instability, in this awesome Medium article by Vinícius Trevisan

Is your ML model stable? Checking model stability and population drift with PSI and CSI

6. Underfitting

Underfitting is the opposite of overfitting, and it occurs when the model is not complex enough to learn from the data. This can happen when the data set is too small, or when the features of the data are not well-represented by the model. To address this issue, you can try to use a more complex model, or increase the size of your data set.

Increase the number of training examples: This will give the model more information to learn from and will help it perform better.

Use a higher resolution dataset: A higher resolution dataset contains more information, which will help the model learn and perform better.

Use a more complex algorithm: Some algorithms are better at handling underfitting than others.

You can read more about how to deal with Underfitting here:

Underfitting

7. Imbalanced datasets

One problem with machine learning is that it can be difficult to train a model when the data set is imbalanced. This can lead to a model that is not accurately predicting results. To address this issue, you can try using more data in your training set or a different algorithm.

Read more about how to deal with imbalanced datasets:

Handling imbalanced datasets in machine learning

You’ve probably heard that machine learning is the future, but how do you get started? Machine learning can be difficult to implement. When faced with a specific problem in your model, try these tips for troubleshooting and getting back on track. Have any of these issues popped up when building your models? Let us know! We’re always here to help our clients overcome roadblocks like this one so they don’t have to go it alone.

This article is brought to you by images.cv,
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images.cv | Image datasets for computer vision and machine learning