Integrating CVAT annotation into MLFlow

Integrating CVAT annotation into MLFlow

CVAT (Computer Vision Annotation Tool) is an open-source annotation tool for computer vision tasks that allows users to label and manage large datasets quickly and efficiently. Integrating CVAT with an MLFlow framework can streamline the data labeling process and make it easier to track and analyze the performance of your machine learning models.

Here is an example of how you can integrate CVAT with an MLFlow framework for both 2D images and point cloud data:

  1. Set up your MLFlow environment by installing MLFlow and creating a new project.
  2. Install CVAT using the instructions provided in the CVAT documentation.
  3. Set up your data pipeline by organizing your datasets into directories and uploading them to the CVAT server. You can use the CVAT API to programmatically upload your data and create new tasks for annotation.
  4. Create a new annotation task in CVAT and assign it to one or more annotators. You can use the web-based interface to label the data, or you can use the CVAT API to automate the labeling process.
  5. As the annotators label the data, they can use the MLFlow integration to track their progress and log any notes or observations about the data.
  6. Once the annotation is complete, you can use the CVAT API to export the labeled data and import it into your MLFlow environment.

For 2D images, you can use the CVAT API to create bounding boxes and classification labels for each image in the dataset. You can then use the labeled data to train a machine learning model using a variety of algorithms and libraries, such as TensorFlow or PyTorch.

For point cloud data, you can use the CVAT API to create 3D bounding boxes and labels for each sensor frame or map tile. You can then use the labeled data to train a 3D object detection or segmentation model using a library like PointNet.

In summary, integrating CVAT with an MLFlow framework can greatly simplify the data labeling process and help you track and analyze the performance of your machine learning models. By using the CVAT API and the MLFlow integration, you can quickly and easily create, label, and manage large datasets for a variety of computer vision tasks.

Installing MLFlow

MLFlow is an open-source platform for managing the end-to-end machine learning lifecycle. It allows you to track and manage your machine learning experiments, package and deploy your models, and serve them in a production environment. In this article, we will walk through the process of setting up your MLFlow environment by installing MLFlow and creating a new project.

Prerequisites

Before we begin, make sure you have the following tools installed on your system:

  • Python 3.6 or above
  • pip (Python package manager)

Installing MLFlow

There are several ways to install MLFlow, but the easiest way is to use pip. Open a terminal and run the following command:

pip install mlflow

This will install MLFlow and all the necessary dependencies.

Creating a New Project

Once MLFlow is installed, you can create a new project by running the following command:

mlflow new myproject

This will create a new directory called myproject with the following structure:

myproject/
├── .gitignore
├── conda.yaml
└── README.md

The conda.yaml file contains the dependencies required to run your project, and the README.md file is a markdown file that you can use to document your project. You can edit these files as needed.

Running MLFlow

To start the MLFlow server, navigate to the root directory of your project and run the following command:

mlflow server

This will start the MLFlow server and open the web interface in your default browser. You should see a page like this:

You can use the web interface to track your experiments, view and compare different runs, and deploy your models.

Installing CVAT

Prerequisites

Before we begin, make sure you have the following tools installed on your system:

  • Docker
  • Docker Compose

Installation

To install CVAT, follow these steps:

  1. Download the latest version of CVAT from the GitHub repository: https://github.com/opencv/cvat
  2. Extract the downloaded archive to a directory of your choice.
  3. Navigate to the cvat/ directory and copy the env-example file to a new file called .env:
cp env-example .env
  1. Open the .env file in a text editor and modify the environment variables as needed. You can find more information about the available options in the CVAT documentation.
  2. Navigate to the cvat/ directory and run the following command to start CVAT:
docker-compose up -d

Configuration

You can configure CVAT by modifying the .env file. Some of the options you can modify include:

  • CVAT_HOST: the hostname or IP address of the CVAT server
  • CVAT_PORT: the port number of the CVAT server
  • CVAT_DEBUG: enable or disable debug mode
  • CVAT_SECRET_KEY: the secret key used for encryption and authentication

Setup CVAT Datapipeline

Once you have CVAT installed, you can set up your data pipeline by organizing your datasets into directories and uploading them to the CVAT server. Using the CVAT API, you can programmatically upload your data and create new tasks for annotation. In this article, we will walk through the process of setting up your data pipeline using the CVAT API.

Prerequisites

Before we begin, make sure you have the following tools installed on your system:

  • Python 3.6 or above
  • pip (Python package manager)
  • The CVAT Python package (pip install cvat-api)

Organizing Your Datasets

The first step in setting up your data pipeline is to organize your datasets into directories. This will make it easier to upload and manage your data in CVAT. Here is an example of how you can organize your datasets:

datasets/
├── dataset1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── dataset2/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
└── ...

Each dataset should contain all the data you want to annotate (e.g., images, videos, etc.). You can organize your data into subdirectories if needed.

Uploading Your Data

Once you have your datasets organized, you can use the CVAT API to upload your data to the CVAT server. Here is an example of how you can upload your data using Python:

import cvat_api

# Create a client object
client = cvat_api.Client()

# Login to the CVAT server
client.login(username='USERNAME', password='PASSWORD')

# Iterate over the datasets
for dataset_dir in datasets:
    # Create a new task
    task = client.tasks.create(name=dataset_dir)
    task_id = task['id']
    
    # Iterate over the files in the dataset directory
    for file in os.listdir(dataset_dir):
        # Upload the file to the CVAT server
        client.tasks.upload(task_id, file)

# Logout from the CVAT server
client.logout()

This code will iterate over the datasets in the datasets directory, create a new task for each dataset, and upload the files to the CVAT server. You can customize the code as needed to fit your specific requirements.

Creating Tasks

In addition to uploading your data, you can also use the CVAT API to create new tasks for annotation. Here is an example of how you can create a new task using Python:

import cvat_api

# Create a client object
client = cvat_api.Client()

# Login to the CVAT server
client.login(username='USERNAME', password='PASSWORD')

# Create a new task
task = client.tasks.create(name='My Task')
task_id = task['id']

# Assign the task to an annotator
client.tasks.assign(task_id, username='ANNOTATOR_USERNAME')

# Logout from the CVAT server
client.logout()

This code will create a new task called “My Task” and assign it to an annotator with the username “ANNOTATOR_USERNAME”. You can customize the task parameters (e.g., name, description, etc.) and assign it to multiple annotators if needed.

Create an Annotation Task

Prerequisites

Before we begin, make sure you have the following tools installed on your system:

  • Python 3.6 or above (if using the CVAT API)
  • pip (Python package manager) (if using the CVAT API)
  • The CVAT Python package (pip install cvat-api) (if using the CVAT API)

Creating a New Task

To create a new annotation task in CVAT, follow these steps:

  1. Log in to the CVAT web interface and click on the “New task” button.
  2. Enter a name and description for the task, and select the type of data you want to annotate (e.g., images, videos, etc.).
  3. Select the data you want to include in the task by dragging and dropping the files or directories into the “Data” section.
  4. Select the annotation tools you want to use (e.g., bounding boxes, classification labels, etc.).
  5. Click the “Create” button to create the task.

Once the task is created, you can assign it to one or more annotators by clicking on the “Assign” button and selecting the annotators from the list.

Labeling the Data

You can label the data in a task using the web-based interface or the CVAT API.

Web-based Interface

To label the data using the web-based interface, follow these steps:

  1. Log in to the CVAT web interface and select the task you want to label.
  2. Click on the “Start annotation” button to open the annotation editor.
  3. Use the annotation tools to label the data (e.g., draw bounding boxes around objects, select classification labels, etc.).
  4. Click the “Submit” button to save your labels and move on to the next data item.

CVAT API

To label the data using the CVAT API, you can use the cvat.tasks.annotations.create function to create new annotations for a task. Here is an example of how you can label the data using Python:

import cvat_api

# Create a client object
client = cvat_api.Client()

# Login to the CVAT server
client.login(username='USERNAME', password='PASSWORD')

# Get the task ID
task_id = 123

# Get the data item ID
data_item_id = 456

# Create a new annotation
annotation = client.tasks.annotations.create(task_id, data_item_id,
    label={"type": "rectangle", "points": [[10, 10], [20, 20]]})

# Logout from the CVAT server
client.logout()

Track Progress in MLFlow

  1. In the CVAT web interface, click on the “Experiments” tab and select the “MLFlow” option from the dropdown menu.
  2. Enter the URL and API token for your MLFlow server, and click the “Connect” button.
  3. As the annotators label the data, they can use the “Log” button to log any notes or observations about the data. These logs will be saved in MLFlow and can be accessed and analyzed later.

Export Your Label Data

Once the annotation is complete in CVAT (Computer Vision Annotation Tool), you can use the CVAT API to export the labeled data and import it into your MLFlow environment. This will allow you to use the labeled data to train and evaluate machine learning models and track your progress using MLFlow.

To export the labeled data from CVAT and import it into MLFlow, follow these steps:

  1. Use the cvat.tasks.annotations.download function to download the labeled data from CVAT as a JSON file:
import cvat_api

# Create a client object
client = cvat_api.Client()

# Login to the CVAT server
client.login(username='USERNAME', password='PASSWORD')

# Get the task ID
task_id = 123

# Download the labeled data
annotations = client.tasks.annotations.download(task_id)

# Save the labeled data to a JSON file
with open('annotations.json', 'w') as f:
    json.dump(annotations, f)

# Logout from the CVAT server
client.logout()

This code will download the labeled data for the task with ID 123 and save it to a JSON file called annotations.json.

  1. Use the mlflow.log_artifact function to log the JSON file as an artifact in MLFlow:
import mlflow

# Set the experiment ID
experiment_id = 123

# Start a new run
with mlflow.start_run(experiment_id=experiment_id):
    # Log the JSON file as an artifact
    mlflow.log_artifact('annotations.json')

This code will log the annotations.json file as an artifact in the run with ID 123 in the experiment with ID 123.

  1. Use the mlflow.log_param and mlflow.log_metric functions to log any relevant parameters and metrics for the run:
import mlflow

# Set the experiment ID
experiment_id = 123

# Start a new run
with mlflow.start_run(experiment_id=experiment_id):
    # Log the JSON file as an artifact
    mlflow.log_artifact('annotations.json')
    
    # Log a parameter
    mlflow.log_param('num_annotations', 1000)
    
    # Log a metric
    mlflow.log_metric('annotation_accuracy', 0.95)

This code will log the annotations.json file as an artifact, and log the num_annotations parameter and the annotation_accuracy metric for the run. You can customize the parameters and metrics as needed to fit your specific requirements.

By using the CVAT API to export the labeled data and the MLFlow API to log the data as artifacts and parameters, you can import the labeled data into your MLFlow environment and use it to train and evaluate machine learning models. For more information, you can refer to the CVAT documentation at https://github.com/opencv/cvat/blob/develop/docs/source/user_guide/index.rst and the MLFlow documentation at https://mlflow.org/docs/latest/index.html.

If you want a managed CVAT / MLFlow Interface, you may use RoadMentor’s managed web interface to run this process end to end without worrying about maintaining the infrastructure or the data management.

 

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