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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/integrations/tensorboard.md
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@ -13,7 +13,7 @@ This guide covers how to use TensorBoard with YOLOv8. You'll learn about various
## TensorBoard
<p align="center">
<img width="640" src="https://www.tensorflow.org/static/tensorboard/images/tensorboard.gif" alt="Tensorboard Overview">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/tensorboard-overview.avif" alt="Tensorboard Overview">
</p>
[TensorBoard](https://www.tensorflow.org/tensorboard), TensorFlow's visualization toolkit, is essential for machine learning experimentation. TensorBoard features a range of visualization tools, crucial for monitoring machine learning models. These tools include tracking key metrics like loss and accuracy, visualizing model graphs, and viewing histograms of weights and biases over time. It also provides capabilities for projecting embeddings to lower-dimensional spaces and displaying multimedia data.
@ -88,7 +88,7 @@ Now, let's focus on understanding the various features and components of TensorB
The Time Series feature in the TensorBoard offers a dynamic and detailed perspective of various training metrics over time for YOLOv8 models. It focuses on the progression and trends of metrics across training epochs. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/ultralytics/assets/25847604/20b3e038-0356-465e-a37e-1ea232c68354)
![image](https://github.com/ultralytics/docs/releases/download/0/time-series-tensorboard-yolov8.avif)
#### Key Features of Time Series in TensorBoard
@ -108,7 +108,7 @@ The Time Series section is essential for a thorough analysis of the YOLOv8 model
Scalars in the TensorBoard are crucial for plotting and analyzing simple metrics like loss and accuracy during the training of YOLOv8 models. They offer a clear and concise view of how these metrics evolve with each training epoch, providing insights into the model's learning effectiveness and stability. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/ultralytics/assets/25847604/f9228193-13e9-4768-9edf-8fa15ecd24fa)
![image](https://github.com/ultralytics/docs/releases/download/0/scalars-metrics-tensorboard.avif)
#### Key Features of Scalars in TensorBoard
@ -140,7 +140,7 @@ While both Scalars and Time Series in TensorBoard are used for tracking metrics,
The Graphs section of the TensorBoard visualizes the computational graph of the YOLOv8 model, showing how operations and data flow within the model. It's a powerful tool for understanding the model's structure, ensuring that all layers are connected correctly, and for identifying any potential bottlenecks in data flow. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/ultralytics/assets/25847604/039028e0-4ab3-4170-bfa8-f93ce483f615)
![image](https://github.com/ultralytics/docs/releases/download/0/tensorboard-yolov8-computational-graph.avif)
Graphs are particularly useful for debugging the model, especially in complex architectures typical in deep learning models like YOLOv8. They help in verifying layer connections and the overall design of the model.