单目3D初始代码
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description: Learn how to set up and run Ultralytics YOLOv5 on AzureML. Follow this quickstart guide for easy configuration and model training on an AzureML compute instance.
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keywords: YOLOv5, AzureML, machine learning, compute instance, quickstart, model training, virtual environment, Python, AI, deep learning, Ultralytics
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---
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# Ultralytics YOLOv5 🚀 on AzureML Quickstart
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Welcome to the Ultralytics [YOLOv5](../../models/yolov5.md) quickstart guide for Microsoft Azure Machine Learning (AzureML)! This guide will walk you through setting up YOLOv5 on an AzureML compute instance, covering everything from creating a virtual environment to training and running inference with the model.
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## What is Azure?
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[Azure](https://azure.microsoft.com/) is Microsoft's comprehensive [cloud computing](https://www.ultralytics.com/glossary/cloud-computing) platform. It offers a vast array of services, including computing power, databases, analytics tools, [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) capabilities, and networking solutions. Azure enables organizations to build, deploy, and manage applications and services through Microsoft-managed data centers, facilitating the migration of workloads from on-premises infrastructure to the cloud.
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## What is Azure Machine Learning (AzureML)?
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[Azure Machine Learning](https://azure.microsoft.com/products/machine-learning) (AzureML) is a specialized cloud service designed for developing, training, and deploying machine learning models. It provides a collaborative environment with tools suitable for data scientists and developers of all skill levels. Key features include [automated machine learning (AutoML)](https://www.ultralytics.com/glossary/automated-machine-learning-automl), a drag-and-drop interface for model creation, and a powerful [Python](https://www.python.org/) SDK for more granular control over the ML lifecycle. AzureML simplifies the process of embedding [predictive modeling](https://www.ultralytics.com/glossary/predictive-modeling) into applications.
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## Prerequisites
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To follow this guide, you'll need an active [Azure subscription](https://azure.microsoft.com/free/) and access to an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2). If you don't have a workspace set up, please refer to the official [Azure documentation](https://learn.microsoft.com/azure/machine-learning/quickstart-create-resources?view=azureml-api-2) to create one.
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## Create a Compute Instance
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A compute instance in AzureML provides a managed cloud-based workstation for data scientists.
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1. Navigate to your AzureML workspace.
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2. On the left pane, select **Compute**.
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3. Go to the **Compute instances** tab and click **New**.
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4. Configure your instance by selecting the appropriate CPU or [GPU](https://www.ultralytics.com/glossary/gpu-graphics-processing-unit) resources based on your needs for training or inference.
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<img width="1741" alt="Azure ML create compute instance interface" src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/create-compute-arrow.avif">
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## Open a Terminal
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Once your compute instance is running, you can access its terminal directly from the AzureML studio.
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1. Go to the **Notebooks** section in the left pane.
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2. Find your compute instance in the top dropdown menu.
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3. Click on the **Terminal** option below the file browser to open a command-line interface to your instance.
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## Setup and Run YOLOv5
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Now, let's set up the environment and run Ultralytics YOLOv5.
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### 1. Create a Virtual Environment
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It's best practice to use a virtual environment to manage dependencies. We'll use [Conda](https://docs.conda.io/en/latest/), which is pre-installed on AzureML compute instances. For a detailed Conda setup guide, see the Ultralytics [Conda Quickstart Guide](../../guides/conda-quickstart.md).
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Create a Conda environment (e.g., `yolov5env`) with a specific Python version and activate it:
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```bash
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conda create --name yolov5env -y python=3.10 # Create a new Conda environment
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conda activate yolov5env # Activate the environment
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conda install pip -y # Ensure pip is installed
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```
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### 2. Clone YOLOv5 Repository
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Clone the official Ultralytics YOLOv5 repository from [GitHub](https://github.com/) using [Git](https://git-scm.com/):
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```bash
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git clone https://github.com/ultralytics/yolov5 # Clone the repository
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cd yolov5 # Navigate into the directory
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# Initialize submodules (if any, though YOLOv5 typically doesn't require this step)
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# git submodule update --init --recursive
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```
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### 3. Install Dependencies
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Install the necessary Python packages listed in the `requirements.txt` file. We also install [ONNX](https://www.ultralytics.com/glossary/onnx-open-neural-network-exchange) for model export capabilities.
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```bash
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pip install -r requirements.txt # Install core dependencies
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pip install "onnx>=1.12.0" # Install ONNX for exporting
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```
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### 4. Perform YOLOv5 Tasks
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With the setup complete, you can now train, validate, perform inference, and export your YOLOv5 model.
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- **Train** the model on a dataset like [COCO128](../../datasets/detect/coco128.md). Check the [Training Mode](../../modes/train.md) documentation for more details.
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```bash
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# Start training using yolov5s pretrained weights on the COCO128 dataset
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python train.py --data coco128.yaml --weights yolov5s.pt --img 640 --epochs 10 --batch 16
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```
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- **Validate** the trained model's performance using metrics like [Precision](https://www.ultralytics.com/glossary/precision), [Recall](https://www.ultralytics.com/glossary/recall), and [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map). See the [Validation Mode](../../modes/val.md) guide for options.
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```bash
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# Validate the yolov5s model on the COCO128 validation set
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python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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```
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- **Run Inference** on new images or videos. Explore the [Prediction Mode](../../modes/predict.md) documentation for various inference sources.
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```bash
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# Run inference with yolov5s on sample images
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python detect.py --weights yolov5s.pt --source data/images --img 640
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```
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- **Export** the model to different formats like ONNX, [TensorRT](https://www.ultralytics.com/glossary/tensorrt), or [CoreML](https://docs.ultralytics.com/integrations/coreml/) for deployment. Refer to the [Export Mode](../../modes/export.md) guide and the [ONNX Integration](../../integrations/onnx.md) page.
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```bash
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# Export yolov5s to ONNX format
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python export.py --weights yolov5s.pt --include onnx --img 640
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```
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## Using a Notebook
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If you prefer an interactive experience, you can run these commands within an AzureML Notebook. You'll need to create a custom [IPython kernel](https://ipython.readthedocs.io/en/stable/install/kernel_install.html) linked to your Conda environment.
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### Create a New IPython Kernel
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Run the following commands in your compute instance terminal:
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```bash
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# Ensure your Conda environment is active
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# conda activate yolov5env
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# Install ipykernel if not already present
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conda install ipykernel -y
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# Create a new kernel linked to your environment
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python -m ipykernel install --user --name yolov5env --display-name "Python (yolov5env)"
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```
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After creating the kernel, refresh your browser. When you open or create a `.ipynb` notebook file, select your new kernel ("Python (yolov5env)") from the kernel dropdown menu at the top right.
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### Running Commands in Notebook Cells
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- **Python Cells:** Code in Python cells will automatically execute using the selected `yolov5env` kernel.
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- **Bash Cells:** To run shell commands, use the `%%bash` magic command at the beginning of the cell. Remember to activate your Conda environment within each bash cell, as they don't automatically inherit the notebook's kernel environment context.
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```bash
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%%bash
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source activate yolov5env # Activate environment within the cell
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# Example: Run validation using the activated environment
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python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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```
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Congratulations! You've successfully set up and run Ultralytics YOLOv5 on AzureML. For further exploration, consider checking out other [Ultralytics Integrations](../../integrations/index.md) or the detailed [YOLOv5 documentation](../index.md). You might also find the [AzureML documentation](https://learn.microsoft.com/en-us/azure/machine-learning/?view=azureml-api-2) useful for advanced scenarios like distributed training or model deployment as an endpoint.
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