Can you use smote on images?

Can you use smote on images?

Synthetic Minority Over-sampling Technique (SMOTE) SMOTE actually performs better than simple oversampling, but although it is not quite popular with images as much as its popularity when dealing with structured data.

How do you oversample a picture?

Oversample

1. 1 – Get labels and their countings.
2. 2 – Define the number of repetitions for each class.
3. 3 – Define the number of repetitions for each training example.
4. 4 – Use data augmentation to avoid (exactly) same images appear too many times.
5. 5 – A method to get oversampled training dataset.
6. 6 – Check oversampled dataset.

Does smote work on categorical data?

So as per documentation SMOTE doesn’t support Categorical data in Python yet, and provides continuous outputs. You can instead employ a workaround where you convert the categorical variables to integers and use SMOTE.

How do you deal with unbalanced image data?

7 Techniques to Handle Imbalanced Data

1. Use the right evaluation metrics.
2. Resample the training set.
3. Use K-fold Cross-Validation in the right way.
4. Ensemble different resampled datasets.
5. Resample with different ratios.
6. Cluster the abundant class.
7. Design your own models.

How to use smote in a two dimensional dataset?

To show how SMOTE works, suppose we have an imbalanced two-dimensional dataset, such as the one in the next image, and we want to use SMOTE to create new data points.

How to use smote for dealing with imbalanced image dataset?

Using it is pretty straight forward. SMOTE is offered by the imblearn package. [**Note** : You might need to reshape your X_train and X_smote depending upon your approach or implementation] Then you can use the SMOTEd data to fit your model.

What can you do with the smote function?

Namely, it can generate a new “SMOTEd” data set that addresses the class unbalance problem. Alternatively, it can also run a classification algorithm on this new data set and return the resulting model.

Can you use smote to fit a model?

Then you can use the SMOTEd data to fit your model. Hence we come to the end of this article. But a word of caution when using SMOTE, sometimes, depending upon the quality of image data and depending upon the size of the data, SMOTE can be extremely slow.

https://www.youtube.com/watch?v=H9wYemw-ZAI