2.8 KiB
| comments | description | keywords |
|---|---|---|
| true | Azure Machine Learning YOLOv5 quickstart | Ultralytics, YOLO, Deep Learning, Object detection, quickstart, Azure, AzureML |
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:
Prerequisites
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 https://github.com/ultralytics/yolov5
cd yolov5
git submodule update --init --recursive # Note that you might have a message asking you to add your folder as a safe.directory 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:
python train.py
Validate the model for Precision, Recall, and mAP
python val.py --weights yolov5s.pt
Run inference on images and videos:
python detect.py --weights yolov5s.pt --source path/to/images
Export models to other formats:
python detect.py --weights yolov5s.pt --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:
%%bash
source activate newenv
python val.py --weights yolov5s.pt