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Remove image "?" args (#15891)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-29 14:11:59 +02:00 • committed by GitHub
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docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
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@ -7,7 +7,7 @@ keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀
<p align="center">
<img width="800" src="https://images.ctfassets.net/2lpsze4g694w/5XK2dV0w55U0TefijPli1H/bf0d119d77faef9a5d2cc0dad2aa4b42/Edge-TPU-USB-Accelerator-and-Pi.jpg?w=800" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
<img width="800" src="https://images.ctfassets.net/2lpsze4g694w/5XK2dV0w55U0TefijPli1H/bf0d119d77faef9a5d2cc0dad2aa4b42/Edge-TPU-USB-Accelerator-and-Pi.jpg" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
</p>
## What is a Coral Edge TPU?

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docs/en/guides/preprocessing_annotated_data.md
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@ -73,7 +73,7 @@ Here are some other benefits of data augmentation:
Common augmentation techniques include flipping, rotation, scaling, and color adjustments. Several libraries, such as Albumentations, Imgaug, and TensorFlow's ImageDataGenerator, can generate these augmentations.
<p align="center">
<img width="100%" src="https://i0.wp.com/ubiai.tools/wp-content/uploads/2023/11/UKwFg.jpg?fit=2204%2C775&ssl=1" alt="Overview of Data Augmentations">
<img width="100%" src="https://i0.wp.com/ubiai.tools/wp-content/uploads/2023/11/UKwFg.jpg" alt="Overview of Data Augmentations">
</p>
With respect to YOLOv8, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.