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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/yolov5/environments/google_cloud_quickstart_tutorial.md
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@ -25,7 +25,7 @@ Let's begin by creating a virtual machine that's tuned for deep learning:
This VM comes loaded with a treasure trove of preinstalled tools and frameworks, including the [Anaconda](https://www.anaconda.com/) Python distribution, which conveniently bundles all the necessary dependencies for YOLOv5.
![GCP Marketplace illustration of setting up a Deep Learning VM](https://user-images.githubusercontent.com/26833433/105811495-95863880-5f61-11eb-841d-c2f2a5aa0ffe.png)
![GCP Marketplace illustration of setting up a Deep Learning VM](https://github.com/ultralytics/docs/releases/download/0/gcp-deep-learning-vm-setup.avif)
## Step 2: Ready the VM for YOLOv5
@ -64,7 +64,7 @@ python export.py --weights yolov5s.pt --include onnx coreml tflite
With just a few commands, YOLOv5 allows you to train custom object detection models tailored to your specific needs or utilize pre-trained weights for quick results on a variety of tasks.
![Terminal command image illustrating model training on a GCP Deep Learning VM](https://user-images.githubusercontent.com/26833433/142223900-275e5c9e-e2b5-43f7-a21c-35c4ca7de87c.png)
![Terminal command image illustrating model training on a GCP Deep Learning VM](https://github.com/ultralytics/docs/releases/download/0/terminal-command-model-training.avif)
## Allocate Swap Space (optional)