{"id":5647324,"date":"2023-02-28T17:12:59","date_gmt":"2023-02-28T22:12:59","guid":{"rendered":"https:\/\/lightning.ai\/pages\/?p=5647324"},"modified":"2023-03-07T16:52:15","modified_gmt":"2023-03-07T21:52:15","slug":"object-detection-cloud","status":"publish","type":"post","link":"https:\/\/lightning.ai\/pages\/community\/object-detection-cloud\/","title":{"rendered":"Object Detection on the Cloud with YOLOv8"},"content":{"rendered":"<p>From non-neural network approaches like <a href=\"https:\/\/en.wikipedia.org\/wiki\/Viola%E2%80%93Jones_object_detection_framework\">Viola\u2013Jones<\/a> to deep neural networks architectures like the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Region_Based_Convolutional_Neural_Networks\">R-CNN family<\/a> and <a href=\"https:\/\/arxiv.org\/abs\/1506.02640\">YOLO model series<\/a>, object detection has come a long way in recent years. YOLO (You Only Look Once), a novel and efficient approach to object detection, was first released in 2015. It gained popularity because, unlike earlier architectures, YOLO could perform the detection as a single network by predicting bounding boxes and class probabilities in a single forward pass. It allowed end-to-end optimization and inspired new model architectures for edge devices. In this blog post, we look at the latest YOLOv8 model released by Ultralytics. This model is a successor to their widely-used YOLOv5 model. In the following, we will explore this model\u2019s new features and cover how you can use <a href=\"https:\/\/lightning.ai\/\">Lightning<\/a> to deploy this model to perform object detection on the cloud.<\/p>\n<div class=\"takeaways card-glow p-4 my-4\"><h3 class=\"w-100 d-block\">Deploy on the cloud<\/h3> Lightning enables you to quickly and easily deploy models like YOLOv8 on the cloud. Learn more about <a href=\"https:\/\/lightning.ai\/pages\/community\/tutorial\/ml-training-deployment\/\">building a training and deployment pipeline<\/a> and <a href=\"https:\/\/lightning.ai\/pages\/community\/tutorial\/dynamic-batching-autoscaling\/\">how to scale your model serving<\/a> with Lightning. <\/div>\n<p>YOLOv5 became widely adopted as a result of its simplicity and performance, and because it requires minimal hyperparameter tuning to train a model. The latest model (YOLOv8) maintains all the excellent features of the previous version and introduces an improved developer experience for the training, finetuning, and deployment of models.<\/p>\n<p><em>The team at YOLOv8 is moving quickly to add new features and will release the paper very soon. Meanwhile, it <a href=\"https:\/\/github.com\/ultralytics\/ultralytics\">is open-source<\/a> and you can start using it right away.<\/em><\/p>\n<p>&nbsp;<\/p>\n<h2>What is Ultralytics?<\/h2>\n<p><a href=\"https:\/\/ultralytics.com\/\">Ultralytics<\/a> is a new computer vision framework by the creators of YOLOv5. It is the result of their learnings from YOLOv5 and continuous research and development efforts. The name of the installable YOLOv8 package is <code>ultralytics<\/code>, and to install the package you can enter <code>pip install ultralytics<\/code> in your terminal.<\/p>\n<div class=\"takeaways card-glow p-4 my-4\"><h3 class=\"w-100 d-block\">Major Ultralytics features<\/h3><\/p>\n<ul>\n<li><strong>Ease of installation<\/strong>: It comes with a <a href=\"https:\/\/pypi.org\/project\/ultralytics\/\">PyPI package<\/a> where you can install the code along with all the dependencies.<\/li>\n<li><strong>Improved command line interface (CLI):<\/strong> The new CLI provides the functionalities to do training, validation, prediction, and model serialization to optimized formats like ONNX and TensorRT.<\/li>\n<li><strong>Python interface<\/strong>: It consists of a Pythonic API that can be used to train models from any Python environment and even in an interactive Jupyter Notebook.<\/li>\n<li><strong>Training support for multiple YOLO versions:<\/strong> With the latest Ultralytics package you can not only train YOLOv8 but it will also support training YOLOv5 and coming versions of YOLO.<\/li>\n<li><strong>Anchorless<\/strong>: An important distinguishing feature of YOLOv8 and YOLOv5 is that it is <a href=\"https:\/\/learnopencv.com\/fcos-anchor-free-object-detection-explained\/\">anchorless<\/a>.<\/li>\n<\/ul>\n<p>More information and docs are available on <a href=\"https:\/\/github.com\/ultralytics\/ultralytics\/\">GitHub<\/a>.<\/p>\n<p><\/div>\n<div class=\"takeaways card-glow p-4 my-4\"><h3 class=\"w-100 d-block\">Best Practices for using the Ultralytics module<\/h3><\/p>\n<ul>\n<li>Save the model as <em><code>model.pt<\/code><\/em> so that you always save your training weights along with the model configuration.<\/li>\n<li>Use default augmentation and hyperparameters if you\u2019re not sure about them<\/li>\n<li>Use streaming mode if you\u2019re running inference on long videos or streams<\/li>\n<\/ul>\n<p><\/div>\n<p>&nbsp;<\/p>\n<h2>YOLOv8<\/h2>\n<p>The following is the benchmark for YOLOv8 against the previous versions on the <a href=\"https:\/\/cocodataset.org\/\">COCO<\/a> dataset.<\/p>\n<div id=\"attachment_5647326\" style=\"width: 2058px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-5647326\" class=\"wp-image-5647326 size-full\" src=\"https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1.png\" alt=\"\" width=\"2048\" height=\"768\" srcset=\"https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1-1024x384@2x.png 2048w, https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1-300x113.png 300w, https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1-1024x384.png 1024w, https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1-1536x576.png 1536w, https:\/\/lightningaidev.wpengine.com\/wp-content\/uploads\/2023\/02\/image1-300x113@2x.png 600w\" sizes=\"(max-width: 2048px) 100vw, 2048px\" \/><p id=\"caption-attachment-5647326\" class=\"wp-caption-text\">Fig 1. YOLOv8 benchmark against previous versions<\/p><\/div>\n<p>&nbsp;<\/p>\n<h2>How to deploy your YOLOv8 model<\/h2>\n<p>So, you\u2019ve trained a custom object detection model. What next?<\/p>\n<p>Let\u2019s deploy this model in such a way that it scales out based on traffic without human interference.<\/p>\n<p>For this tutorial, we\u2019ll export the model to <a href=\"https:\/\/pytorch.org\/docs\/stable\/jit.html\">TorchScript<\/a> format. TorchScript is a serializable and optimizable format for PyTorch code. A benefit of using this format is that it <a href=\"https:\/\/pytorch.org\/tutorials\/recipes\/distributed_optim_torchscript.html#what-is-distributed-optimizer-with-torchscript-support\">releases the GIL<\/a> lock.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Using the CLI:<\/strong><\/p>\n<pre class=\"code-shortcode dark-theme window- collapse-false \" style=\"--height:falsepx\"><code class=\"language-python\">\n\nyolo export model=yolov8n.pt format=torchscript\n\n<\/code><div class=\"copy-button\"><button class=\"expand-button\">Expand<\/button><button class=\"copy\">Copy<\/button><\/div><\/pre>\n<p>&nbsp;<\/p>\n<p><strong>With a Python API:<\/strong><\/p>\n<pre class=\"code-shortcode dark-theme window- collapse-false \" style=\"--height:falsepx\"><code class=\"language-python\">\n\nmodel.export (format=\"torchscript\")\n\n<\/code><div class=\"copy-button\"><button class=\"expand-button\">Expand<\/button><button class=\"copy\">Copy<\/button><\/div><\/pre>\n<p>&nbsp;<\/p>\n<p>After exporting, we build our model server. We\u2019ll be using the <code>PythonServer<\/code> component (a reusable and customizable unit for building distributed Python applications) to deploy the model.<\/p>\n<p>To build a model server using <code>PythonServer<\/code>, we need to create a class inheriting <code>PythonServer<\/code> and implement two abstract methods: <code>setup(...)<\/code> and <code>predict(...)<\/code>.<\/p>\n<p>We create our model inside the setup method and write the prediction logic inside the <code>predict<\/code> method:<\/p>\n<pre class=\"code-shortcode dark-theme window- collapse-false \" style=\"--height:falsepx\"><code class=\"language-python\">\n\n# !pip install ultralytics lightning-api-access<br \/>\n# !pip uninstall -y opencv-python opencv-python-headless<br \/>\n# !pip install opencv-python-headless==4.5.5.64\n\nimport lightning as L<br \/>\nfrom lightning.app.components.serve import PythonServer<br \/>\nfrom pydantic import BaseModel\n\nclass InputType(BaseModel):<br \/>\nimage_url: str\n\nclass Detections(BaseModel):<br \/>\nprediction: list\n\nclass YoloV8Server(PythonServer):<br \/>\ndef setup(self):<br \/>\nfrom ultralytics import YOLO<br \/>\nself._model = YOLO(\"yolov8n.pt\")\n\ndef predict(self, request: InputType):<br \/>\npreds = self._model.predict(request.image_url)[0]<br \/>\nclasses = preds.boxes.cls<br \/>\nresults = [self._model.names[int(cls)] for cls in classes]<br \/>\nreturn {\"prediction\": results}\n\ncomponent = YoloV8Server(input_type=InputType, output_type=Detections)<br \/>\napp = L.LightningApp(component)\n\n<\/code><div class=\"copy-button\"><button class=\"expand-button\">Expand<\/button><button class=\"copy\">Copy<\/button><\/div><\/pre>\n<p>&nbsp;<\/p>\n<p>To run the model server open the terminal and enter <code>lightning run app app.py<\/code>. A browser tab will open with the API documentation.<\/p>\n<p>To deploy this application on the cloud, simply append the <code>cloud<\/code> flag and your model will be deployed on the Lightning AI cloud platform:<\/p>\n<pre class=\"code-shortcode dark-theme window- collapse-false \" style=\"--height:falsepx\"><code class=\"language-python\">\n\nlightning run app app.py --cloud\n\n<\/code><div class=\"copy-button\"><button class=\"expand-button\">Expand<\/button><button class=\"copy\">Copy<\/button><\/div><\/pre>\n<p>&nbsp;<\/p>\n<p>You can add additional power to your server by enabling AutoScaling, a Lightning component that scales the model server automatically based on traffic. To use AutoScaler, we need to import the component and move your <code>YoloV8Server<\/code> inside the AutoScaler component:<\/p>\n<pre class=\"code-shortcode dark-theme window- collapse-false \" style=\"--height:falsepx\"><code class=\"language-python\">\n\nfrom lightning.app.component import AutoScaler\n\n...\n\ncomponent = AutoScaler(Yolov8Server, input_type=InputType, output_type=Detections)\n\napp = L.LightningApp(component)\n\n<\/code><div class=\"copy-button\"><button class=\"expand-button\">Expand<\/button><button class=\"copy\">Copy<\/button><\/div><\/pre>\n<p>&nbsp;<\/p>\n<p>And that\u2019s it! Now we\u2019ve built a custom model server, deployed it on the cloud, and even introduced automated scaling based on traffic in just a few lines of code.<\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: center;\"><div class=\"takeaways card-glow p-4 my-4\"><h3 class=\"w-100 d-block\">Deploy on the cloud<\/h3> Lightning gives you thirty free credits every month that you can use to deploy models like YOLOv8 on the cloud.<\/p>\n<p style=\"text-align: center;\"><\/div>\n<p style=\"text-align: center;\"><a target=\"blank\" href=\"https:\/\/lightning.ai\" class=\"d-inline-block btn btn-\">Sign up for Lightning!<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>From non-neural network approaches like Viola\u2013Jones to deep neural networks architectures like the R-CNN family and YOLO model series, object detection has come a long way in recent years. YOLO (You Only Look Once), a novel and efficient approach to object detection, was first released in 2015. It gained popularity because, unlike earlier architectures, YOLO<a class=\"excerpt-read-more\" href=\"https:\/\/lightning.ai\/pages\/community\/object-detection-cloud\/\" title=\"ReadObject Detection on the Cloud with YOLOv8\">&#8230; Read more &raquo;<\/a><\/p>\n","protected":false},"author":16,"featured_media":5647328,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[106,41],"tags":[],"glossary":[],"acf":{"additional_authors":[{"author_name":"Ayush Chaurasia","author_url":"https:\/\/twitter.com\/loldedxd"}],"hide_from_archive":false,"content_type":"Blog Post","custom_styles":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Object Detection on the Cloud with YOLOv8<\/title>\n<meta name=\"description\" content=\"Learn about the latest YOLOv8 model and 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