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YOLOv5 🚀 on AzureML

This guide provides a quickstart to use YOLOv5 from an AzureML compute instance.

Note that this guide is a quickstart for quick trials. If you want to unlock the full power AzureML, you can find the documentation to:


You need an AzureML workspace.

Create a compute instance

From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.


Open a Terminal

Now from the Notebooks view, open a Terminal and select your compute.


Setup and run YOLOv5

Now you can, create a virtual environment:

conda create --name yolov5env -y
conda activate yolov5env
conda install pip -y

Clone YOLOv5 repository with its submodules:

git clone
cd yolov5
git submodule update --init --recursive # Note that you might have a message asking you to add your folder as a just copy the recommended command

Install the required dependencies:

pip install -r yolov5/requirements.txt
pip install onnx>=1.10.0

Train the YOLOv5 model:


Validate the model for Precision, Recall, and mAP

python --weights

Run inference on images and videos:

python --weights --source path/to/images

Export models to other formats:

python --weights --source path/to/images

Notes on using a notebook

Note that if you want to run these commands from a Notebook, you need to create a new Kernel and select your new Kernel on the top of your Notebook.

If you create Python cells it will automatically use your custom environment, but if you add bash cells, you will need to run source activate <your-env> on each of these cells to make sure it uses your custom environment.

For example:

source activate newenv
python --weights

Created 2023-11-12, Updated 2024-01-07
Authors: glenn-jocher (2), ouphi (1)