November 9, 2021
Leverage GitLab’s powerful CI/CD pipelines and Comet’s Experiment Management capabilities to create a model development…
To jump directly into resources about how to use Comet and Yolov5, check out:
Start training and logging Yolov5 models with Comet:
The Yolov5 library is a family of deep learning object detection architectures that are pre-trained on the COCO dataset. It’s open-source and feature-rich. To date, it’s become one of the most practical set of object detection algorithms that anyone can use.
Computer vision use cases are costly to train. Handling unstructured data and debugging the model performance is also complicated. Yolov5 makes it simple to apply computer vision to any custom application.
Want to learn more about Yolo object detection more generally? Check out this YouTube video tutorial.
Yolov5 is a library with a suite of tools that enables both beginners and experts of object detection to train and tune a model for production-ready performance. Some of the unique features include:
The Ultralytics team that manages Yolov5 has put a lot of effort into their documentation, along with adding integrations and tutorials.
The Yolov5 library can be a great starting point to your computer vision journey. To improve the model’s performance and get it production-ready, you’ll need to log the results in an experiment tracking tool like Comet.
The Comet and Yolov5 integration offers 3 main features that we’ll cover in this post:
Comet is a powerful tool for tracking your models, datasets, and metrics. It even logs your system and environment variables to ensure reproducibility and smooth debugging for each and every run. It’s like having a virtual assistant that magically knows what notes to keep.
With the Yolov5 integration, Comet automatically logs each of the following, straight out-of-the-box, and without any additional code:
If you’re looking for more in-depth experiment management, custom logging capabilities are also available either through command line flags or environment variables. With Comet, you can log custom user-defined metrics, class-level metrics, and model predictions.
Log Yolov5 checkpoints in Comet
Training with unstructured data (like images) can be painfully time-consuming. Any interruption can be a major set-back, especially if you have to start training from scratch.
By logging checkpoints, you can simply pick-up where you left off! Comet allows you to resume training from your latest checkpoint, specify which checkpoints are logged, overwrite checkpoints, and retrieve saved checkpoints with a simple command line flag.
Log datasets and Yolov5 models as Comet Artifacts
Saving datasets and models as Artifacts in Comet will support you with debugging and reproducibility. You can upload artifacts in isolation (e.g. a dataset you plan on using later, or a pre-trained model), or you can upload them automatically with your training runs, all with a simple command line flag.
To log model predictions as images, you can add
With Comet Artifacts, you can:
Once you’ve logged an artifact with Comet, you can upload a version of it with:
To see any of the custom logging in action, check out the Colab.
Comet’s platform lets you visualize your model in any way you like. You can choose from any of Comets 200+ publicly available Panels or build your own!
Now that you know what the integration can do, why not see it for yourself? Our resident data scientist, Dhruv Nair has logged a Yolov5 model.
When you navigate to Dhruv’s public experiment in the Comet UI, your default view displays Panels that illustrate performance metrics across multiple experiment runs, helping you visually compare models and experiments within a single project. Comet’s Yolov5 integration automatically logs experiment metrics like precision, mean average precision (mAP), recall, and training loss, and then plots them for you in the default panel view. Additionally, if you select individual experiments, you’ll find that Comet auto-logs even more details of each specific experiment run like system metrics and package installations, learning rate, loss metrics, and more.
With Comet, you also have the freedom to further customize which metrics and features are logged. For most experiment-specific logging, just insert the relevant command line flag or environment variable into your original code from this simple documentation. This panel illustrates your model’s bounding box predictions on validation images, and allows you to adjust confidence thresholds and filter by label– all directly in the UI!
Since you’re still here, thanks for reading this far! Check out these free resources to help debug your Yolov5 model.
You can also join Comet’s Slack community to get support on any integration.