Computer vision is a rapidly growing interdisciplinary field of artificial intelligence that involves analyzing and interpreting digital images, videos and other visual inputs. As the field continues to grow, there is a lot of investment in terms of time and effort to improve the models.
This is typically achieved through iterative and experimental processes such as changing the model, running multiple experiments, and examining the results, then deciding whether the recent change in the model was positive and should be kept or discarded.
These cycles of experimenting can cause a great deal of confusion. It is easy to forget the changes you made in a certain experiment and whether the recent results are indeed better than the previous ones. Single experiments can take hours to complete and so you will try to save your time and execute multiple experiments simultaneously. This makes it even less manageable, and the confusion gets even worse.
Experiment tracking provides the solution to this confusion. It involves keeping track of important information about different experiments when developing models. The information to be tracked could be:
- Different versions of training data
- Hyperparameters of the model
- Different ML models
- Codes used in model development
Additionally, for some projects, you might track yet more information such as model weights, prediction distributions, model checkpoints, and hardware resources.
Experiment tracking is implemented by ML teams in a variety of ways, including using GitHub, spreadsheets, or in-house platforms. However, using tools which are specifically for tracking and managing ML experiments is the most efficient choice.
In this article, we discuss top tools which will help you track and manage your computer vision experiments.
Top experiment tracking tools
- Picsellia
Picsellia is an end-to-end MLOps platform dedicated to Computer Vision. It aims to empower technical teams, particularly those experienced in CV modeling, to transition their models into remarkable Computer Vision Pipelines.
Picsellia goes beyond mere experiment tracking and offers a complete CVOps toolchain.
It acts as a centralized platform for collaboration, centralization, and automation, similar to how GitLab serves as a platform for software development. Picsellia enables organizations to build remarkable machine learning pipelines for Computer Vision, providing operability and observability throughout the process.
Advantages:
- Enhanced Model Validation and Optimization: Picsellia's dashboards provide valuable insights into accuracy, CPU usage, and other metrics, enabling you to validate your models and optimize settings. This empowers you to fine-tune your computer vision models for better performance.
- Scalability for Large Datasets: Picsellia is renowned for its ability to support very large datasets, making it a favoured choice among computer vision researchers and practitioners. It can handle and process massive amounts of data efficiently, ensuring seamless experimentation and analysis.
- Centralized Model Consolidation and Collaboration: With Picsellia, you can consolidate all your computer vision models in a centralized location. This enables your team to collaborate effectively by leaving comments, using mentions, and receiving notifications. The sharing functionality facilitates seamless teamwork and knowledge exchange.
- Secure Archiving of Experiments: Picsellia's archiving feature ensures that all your experiments are stored in a single place, providing peace of mind that your valuable data is safe and accessible whenever you need it. This eliminates the risk of losing important experiment data and allows for easy retrieval and analysis.
- Enhanced Observability in Production: Picsellia enables you to observe your AI models in production, driving a higher return on investment (ROI). By gaining full observability into deployed models and the live data that feed them, you can proactively monitor and optimize the performance of your computer vision models, reducing the risk of project failure and ensuring successful outcomes.
- Neptune
Neptune is a powerful yet lightweight experiment tracking tool that's ideal for managing machine learning experiments, including those related to computer vision. It's designed to help both research and production teams that run a large number of experiments.
Neptune enables you to easily monitor, debug, visualize, and compare thousands of machine learning models, all in one place.
Advantages:
- By visualizing training live in the Neptune web app, users can see how different parameters and configurations impact the results, leading to faster optimization of models.
- Having a unified platform where your team can view and access all models and experiments in one place, supports collaboration and sharing of experiment results and models across your organization with ease.
- Convenient and easy to use UI/UX which allows you to compare experiments and also create customized dashboards.
- Neptune supports integration with a variety of MLOps tool stack from data ingestion and preprocessing tools to model deployment and serving.
- MLflow
MLflow is an open-source platform that supports the entire machine learning lifecycle. It consists of four main components: Tracking, Model Registry, Projects, and Models. The Tracking component includes both an API and UI for logging metadata such as metrics, code versions, and parameters. You can then easily visualize the results.
Advantages:
- It is highly customizable to fit your workflow because it is an open-source project.
- Has a very large and active community behind it and is very widely adopted in the industry.
- It is easily integrable with any ML library, language, deployment tool or algorithm because of an open interface.
- Weight and Biases
Weight and Biases is a platform built for machine learning experiment tracking, data versioning and model management. It helps you track, log, compare, and visualize the information you need for your computer vision projects.
It is available both in the cloud and on-premise and it is easily integrable with other frameworks and libraries such as Keras, Pytorch, Tensorflow, Scikit-learn and more.
Advantages:
- Weights & Biases supports a wide range of various frameworks and libraries in terms of integrations, including Keras, the PyTorch environment, TensorFlow, Fastai, Scikit-learn, and more.
- Interactive and customizable UI which gives you the flexibility of organizing and visualizing your computer vision workflows.
- With weights & biases, you can visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources.
- Weight and Biases is accessible from anywhere, allowing you to easily check the latest training models and results from your desktop or mobile device.
- It supports collaborative hosted projects to enable seamless coordination across teams, ensuring everyone has access to the same information and can contribute to experiments no matter where they are located.
- Comet
Comet is a platform agnostic ML platform that allows you to track, compare, explain and optimize experiments and models from training to production. It is available for teams, individuals, and organizations who want to manage their experiments including computer vision experiments.
It can be used as a self-hosted platform or cloud-based.
Advantages:
- Comet allows you to track and share training run results in real-time, providing visibility into training runs and models that helps you iterate faster and make more informed decisions.
- Comet offers built-in and customizable visualizations using Plotly and Matplotlib to create interactive and tailored data visualizations.
- Comet's ML platform monitors deployed models for performance degradation caused by data drift or quality issues. Its automatic baseline tracking helps detect drift and track accuracy metrics to maintain optimal model performance.
- It has dedicated modules for visual, audio, text and tabular data that help you identify issues with your dataset.
Final Thoughts
Tracking and managing your computer vision experiments in an organized and efficient way is very important. It can be frustrating when trying to recreate a model from days ago that yielded the best results. Optimizing experiment tracking for your computer vision projects can help avoid this issue.
In this blog post, we explore the top 5 tools that can assist you in tracking your computer vision experiments. We hope this post brings positive change to your computer vision projects and machine learning experiments overall.
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