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Optimize Docs images (#15900)

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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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Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
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docs/en/integrations/tflite.md
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@ -7,7 +7,7 @@ keywords: YOLOv8, TFLite, model export, TensorFlow Lite, edge devices, deploymen
# A Guide on YOLOv8 Model Export to TFLite for Deployment
<p align="center">
<img width="75%" src="https://github.com/ultralytics/ultralytics/assets/26833433/6ecf34b9-9187-4d6f-815c-72394290a4d3" alt="TFLite Logo">
<img width="75%" src="https://github.com/ultralytics/docs/releases/download/0/tflite-logo.avif" alt="TFLite Logo">
</p>
Deploying computer vision models on edge devices or embedded devices requires a format that can ensure seamless performance.
@ -41,7 +41,7 @@ TFLite offers various on-device deployment options for machine learning models,
- **Deploying with Android and iOS**: Both Android and iOS applications with TFLite can analyze edge-based camera feeds and sensors to detect and identify objects. TFLite also offers native iOS libraries written in [Swift](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/swift) and [Objective-C](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/objc). The architecture diagram below shows the process of deploying a trained model onto Android and iOS platforms using TensorFlow Lite.
<p align="center">
<img width="75%" src="https://1.bp.blogspot.com/-6fS9FD8KD7g/XhJ1l8y2S4I/AAAAAAAACKw/MW9MQZ8gtiYmUe0naRdN0n2FwkT1l4trACLcBGAsYHQ/s1600/architecture.png" alt="Architecture">
<img width="75%" src="https://github.com/ultralytics/docs/releases/download/0/architecture-diagram-tflite-deployment.avif" alt="Architecture">
</p>
- **Implementing with Embedded Linux**: If running inferences on a [Raspberry Pi](https://www.raspberrypi.org/) using the [Ultralytics Guide](../guides/raspberry-pi.md) does not meet the speed requirements for your use case, you can use an exported TFLite model to accelerate inference times. Additionally, it's possible to further improve performance by utilizing a [Coral Edge TPU device](https://coral.withgoogle.com/).