Thread by @weights_biases

We're thrilled to announce that YOLOv5 now comes with @weights_biases baked in!

With no additional lines of code, you now get automatic bounding box debugging, GPU usage and performance metrics, reproducible models, & more!

👩‍🚀 Try it → colab.research.google.com/github/ultraly…

#deeplearning

What does YOLOv5 + W&B give you?

1. You can monitor how your models and hyperparameters are performing, including automatically tracking:

- Training and validation losses
- Precision, Recall, mAP@0.5, mAP@0.5:0.95
- Learning Rate over time

2. Automatically tracked system metrics like GPU Type, GPU Utilization, power, temperature, CUDA memory usage; and system metrics like Disk I/0, CPU utilization, RAM memory usage.

3. Reproducible results that make it easy to build on your own models or collaborate across teams. W&B tracks your training environment – OS and Python types, Git repository and state, training command.

4. Share model insights with your team, or the whole world.

W&B Reports combile interactive plots with explanatory text to help you tell the story of your model in an powerful way.

Here is an example report created from the COCO128 tutorial training of all four YOLOv5 models.

5. Bounding Box Debugging

Judging object detection models manually is painful. With YOLOv5, you get an interactive bounding box debugging plot where you can play around with confidence parameters to choose the optimal model and thresholds.

Here's an example:

6. Get started in 5 mins!

👨‍🚀 Try the integration colab.research.google.com/github/ultraly…

👩‍🚀 Train on a custom dataset github.com/ultralytics/yo…

Thank you to the @ultralytics team, & to @ayushex for their work on this integration!

We can't wait for you to try it!

#deeplearning #machinelearning