Training a Model in MLFlow from CVAT label data
CVAT (Computer Vision Annotation Tool) is an open source tool developed by Intel that allows users to label and annotate images and video data for training machine learning models. MLFlow is an open source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiment runs, organizing code, and reproducing runs, among other features.
In this article, we will discuss how to take the output of CVAT and use it to retrain a machine learning model using MLFlow.
Step 1: Export the annotations from CVAT
The first step in this process is to export the annotations that have been created using CVAT. This can be done by clicking the “Export” button in the CVAT interface and selecting the desired format for the annotations (e.g., XML or JSON).
Step 2: Preprocess the annotations
Once the annotations have been exported, the next step is to preprocess them for use in training a machine learning model. This may involve a number of tasks, such as parsing the annotation files, extracting the relevant data, and possibly transforming or normalizing the data in some way.
Step 3: Split the data into training and test sets
Once the annotations have been preprocessed, the next step is to split the data into training and test sets. This is typically done by randomly selecting a portion of the data to be used for training and using the remaining data for testing. The specific ratio of training to test data will depend on the needs of the particular machine learning task at hand.
Step 4: Train a machine learning model
With the training and test data prepared, the next step is to train a machine learning model using the training data. This can be done using any number of machine learning frameworks or libraries, such as TensorFlow, PyTorch, or scikit-learn.
Step 5: Evaluate the model
Once the model has been trained, it is important to evaluate its performance on the test data to see how well it generalizes to new, unseen data. This can be done by using a number of different evaluation metrics, such as accuracy, precision, and recall, depending on the needs of the particular machine learning task.
Step 6: Save the model using MLFlow
Once the model has been trained and evaluated, the final step is to save it using MLFlow. This can be done by using the MLFlow Python API to log the model and its associated metadata (e.g., training data, evaluation metrics, etc.) to an MLFlow server. This allows the model to be easily shared and deployed to other systems or environments for use.
In conclusion, taking the output of CVAT and retraining a model using MLFlow is a simple process that involves exporting annotations from CVAT, preprocessing the data, splitting the data into training and test sets, training a machine learning model, evaluating the model, and saving the model using MLFlow. By following these steps, users can easily use CVAT to label and annotate data for training machine learning models, and then use MLFlow to manage the end-to-end machine learning lifecycle.