{ "source_file": "0930", "source_platform": "Medium", "author": "Ivo Bernardo", "title_listed": "Easy Object Detection with Yolo-NAS", "keywords": "", "domain": "图像处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/easy-object-detection-with-yolo-nas-51f9931be979/", "publish_date": "2024-08-04", "corpus_stage": "", "notes": "", "id": "8d350e675d67", "collected_at": "2026-10-02T05:49:51", "title": "Easy Object Detection with Yolo-NAS | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Easy Object Detection with Yolo-NAS\nLearn how to do object detection with Python using yolo-NAS\n[Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\nAugust 3, 20246 min read\n![Photo by googledeepmind @ Unplash.com](../images/000.jpg)\nPhoto by [googledeepmind](https://unsplash.com/pt-br/@googledeepmind) @ Unplash.com\nYOLO (_You only look once_) revolutionized the computer vision arena. The first version of YOLO [was released in 201](https://arxiv.org/abs/1506.02640)6 by Joseph Redmon et. al and it smashed benchmarks both in terms of speed and accuracy. When it comes to object detection, YOLO has been a favorite of Data Scientists and Machine Learning engineers and the go-to model when it comes to segmenting entities in images.\nSince it was launched, YOLO had many new iterations that improved several setbacks of previous versions, namely:\n * Improved architecture of the underlying deep learning models.\n * Implemented alternatives to improve performance, such as data augmentation techniques.\n * Migrated the original YOLO code to use _pytorch_ training and deployment frameworks.\n * Improved detection mechanisms of small objects.\n\n\nThe last version of YOLO is YOLO v9 (). one important thing to be aware is that every computer vision and object detection model is evaluated on two parameters: **Accuracy** (defined by metrics related to computer vision segmentation) __ and **Speed** (defined by latency in the inference). One example of how CV algorithms are evaluated is shown below:\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n![Comparison of different YOLO models vs. EfficientDet - Image by https://blog.roboflow.com/yolov5-is-here/](../images/001.png)\nComparison of different YOLO models vs. EfficientDet - Image by \nIn this example, I'm going to show you how to run your own YOLO models on images and videos, so that you can rform object detection and segmentation. The entire code I'm executing is linked here, on this [Google Colab Notebook](https://colab.research.google.com/drive/1haLyNBz1i-bhh4GPizY-vsRABXs7srOQ?usp=sharing).\nPart of the code and blog post was inspired by this [great notebook](https://colab.research.google.com/drive/1q0RmeVRzLwRXW-h9dPFSOchwJkThUy6d?usp=sharing) by [Harpreet Sahota](https://datascienceharp.medium.com/).\nLet's start!\n* * *\n## Loading the Model\nWe'll start by training our own computer vision model...\nOf course not, that would be a waste of time :-)\nWe'll start by loading the pre-trained version of a large YOLO model. In this case, we'll use an implementation of YOLO with NAS ([Neural Architecture Search](https://en.wikipedia.org/wiki/Neural_architecture_search)). Neural architecture search is a common implementation of neural network optimization techniques to improve the automatic choice of parameters in the deep learning model.\nThe model we'll use was trained on the COCO dataset ([common objects in context](https://cocodataset.org/#home)). These weights already contain a very good baseline to detect objects on images.\nFirst, let's add a couple of libraries we will need to our environment, namely:\n * `torchinfo` , a helper to visualize our neural network architecture;\n * `super_gradients` , the library we will use to load our models;\n\n\ndiff\n\n```\n!pip install torchinfo\n!pip install super_gradients\n```\n\nNext, we'll load the functions and libraries we will need:\njavascript\n\n```\nfrom super_gradients.training import models\nfrom torchinfo import summary\nimport torch\n```\n\n_Note: If you are using google colab and have a prompt to restart the kernel after installing `super_gradients` , do it. It's a known issue from using `super_learners` in that environment_\nThen, we start by loading our COCO pretrained model into memory using `super_gradients` :\nini\n\n```\n# Using Yolo NAS Large\nyolo_nas_l = models.get(\"yolo_nas_l\", pretrained_weights=\"coco\")\n```\n\nCool! We have our pretrained model stored in `yolo_nas_l` . Picking up these pretrained versions of YOLO is super easy with `models` .\n`torchinfo` gives us a very cool view of our model architecture:\npython\n\n```\nsummary(model=yolo_nas_l,\n input_size=(16, 3, 640, 640),\n col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n col_width=20,\n row_settings=[\"var_names\"]\n)\n```\n\nOut of curiosity, if you would like to check the YOLO architecture (from the original paper) check out the image below:\n![Image Source: https://www.researchgate.net/publication/329038564_Complexity_and_accuracy_analysis_of_common_artificial_neural_networks_on_pedestrian_detection](../images/002.png)\nImage Source: \nHere's a preview of the architecture of our YOLO NAS model:\n![Preview of Yolo-NAS model - image by author.](../images/003.png)\nPreview of Yolo-NAS model - image by author.\nWith the YOLO weights loaded, it's time to play around with image segmentation!\n* * *\n## Segmenting Images\nLet's start with a simple image featuring a desk as the centerpiece, surrounded by various objects. In this image, we can see:\n * Two chairs\n * A desk\n * Two Lamps\n * One computer\n * Headphones\n * One plant\n\n\n![Image 1 - Image by dawidliberadzki at Unsplash](../images/004.jpg)\nImage 1 - Image by [dawidliberadzki](https://unsplash.com/pt-br/@dawidliberadzki) at Unsplash\nIn our code, we only request bounding boxes where our model has a confidence level exceeding 55%. Only labels for these items will show up in the segmented image.\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1476365518243-f738bf58443d?q=80&w=2787&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.55).show()\n```\n\n![Labeled Image with YOLO - Image by Author](../images/005.png)\nLabeled Image with YOLO - Image by Author\nNice! Our model was able to confidently detect the chairs, laptop, desk and plant (although it missed the lamps, headphones and that hi-tech jug). Let's see what happens if we lower our confidence threshold:\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1476365518243-f738bf58443d?q=80&w=2787&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.40).show()\n```\n\n![Labeled Image with YOLO \\(40% Confidence\\) - Image by Author](../images/006.png)\nLabeled Image with YOLO (40% Confidence) - Image by Author\nAh, interesting! Lowering our threshold has led to our first instance of misclassification. In this case, the headphones were incorrectly identified as a computer mouse. Reducing the threshold will inevitably result in more errors in object detection and segmentation.\nLet's try an image with a more crowded scenario:\n![Image by madebyvadim @unsplash.com](../images/007.jpg)\nImage by [madebyvadim](https://unsplash.com/pt-br/@madebyvadim) @unsplash.com\nWhat objects will our YOLO model segment? As this image has more elements, I'll lower the confidence of the inference:\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1496664444929-8c75efb9546f?q=80&w=2940&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.3).show()\n```\n\n![Labeled Image with YOLO \\(30% Confidence\\) - Image by Author](../images/008.png)\nLabeled Image with YOLO (30% Confidence) - Image by Author\nAs we can see, YOLO fails to segment some of the objects - particularly smaller / thiner objects. Nevertheless, performance is quite good, particularly for such a crowded image. To improve some the performance, [transfer learning with images related](https://kikaben.com/yolov5-transfer-learning-dogs-cats/) to your use case is extremely important.\n**Now, with**`**super_gradients**`**, we're abstracting most of code that actually prepares and predicts the boundaries on our image.** If you would like to have a deeper insight on the foundations of these models, check out the article [_Training a Computer Vision Algorithm from my Pytorch Introduction Series_](https://medium.com/towards-data-science/pytorch-introduction-training-a-computer-vision-algorithm-c17ed96e64d5?sk=74bd56317df5a7910d5f0efa5578b7dc) _._\nIn the next section, we'll learn how to use YOLO-NAS, but in the context of segmenting videos.\n* * *\nFor this section, I'll be using a traffic short video on Youtube:\n![Traffic Video - https://www.youtube.com/watch?v=CftLBPI1Ga4](../images/009.png)\nTraffic Video - \nWe'll start by downloading this video into our Google Colab environment. We can also display the video by using `IPython` 's handy `YoutubeVideo` function:\njava\n\n```\nfrom IPython.display import YouTubeVideo\n\nvideo = YouTubeVideo('CftLBPI1Ga4')\ndisplay(video)\n```\n\nTo download our video, I'll use [_youtube-dl_](https://github.com/ytdl-org/youtube-dl):\nbash\n\n```\nvideo_url = f'https://www.youtube.com/watch?v=CftLBPI1Ga4'\n\n!pip install -U \"git+https://github.com/ytdl-org/youtube-dl.git\"\n!python -m youtube_dl -f 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4' \"$video_url\"\n```\n\nWe have almost everything ready. We just need to set the output path for the segmented video and we're ready to pass our video through the YOLO model!\nini\n\n```\ninput_video_path = f\"/content/Cars, Busy Streets, City Traffic - No Copyright Royalty Free Stock Videos-CftLBPI1Ga4.mp4\"\noutput = \"traffic.mp4\"\n```\n\nAll set! Let's run our segmentation model:\nlua\n\n```\ndevice = 'cuda' if torch.cuda.is_available() else \"cpu\"\nyolo_nas_l.to(device).predict(input_video_path).save(output)\n```\n\n_Note: This code may take a bit to run, depending on the configurations of your local system and the availability of GPUs on Google Colab_\n.. and that's it! You can check out the segmented video on [my youtube channel \"The Data Journey\"](https://youtu.be/-7j4eJorJSY). Here's a cool preview:\n![Segmented Traffic Video - Image by Author](../images/010.png)\nSegmented Traffic Video - Image by Author\n* * *\nYOLO models are extremely powerful! You can use them to segment images or videos quite fast with just a couple lines of code. Using this code that I've shown here, it's super easy to play around with your own media and experiment with this great computer vision model.\nAs you may noticed, particularly in complex images or videos, YOLO base models still have some flaws that need to be corrected. As I've mentioned this can be improved quite well with model fine tuning. Check out this [post](https://medium.com/@yongsun.yoon/fine-tuning-yolov8-using-custom-dataset-generated-by-open-world-object-detector-5724e267645d) for more info.\nAnd, if you are curious on how the building blocks of Deep Learning work, feel free to check my _pytorch_ introduction series, that should give you a good overview on the basics of deep learning:\n * [Tensor Basics](https://towardsdatascience.com/pytorch-introduction-tensors-and-tensor-calculations-412ff818bd5b?sk=2cf4d44549664fc647baa3455e9d78e8);\n * [Create a simple linear model using Pytorch.](https://towardsdatascience.com/pytorch-introduction-building-your-first-linear-model-d868a8681a41)\n * [Learn how to work with non-linear activation functions and how to solve non-linear problems.](https://towardsdatascience.com/pytorch-introduction-enter-nonlinear-functions-4dd893845592)\n * [Using custom data.]( seen how to use custom data in the context of the library.)\n * [Training a computer vision algorithm.](https://medium.com/towards-data-science/pytorch-introduction-training-a-computer-vision-algorithm-c17ed96e64d5?sk=74bd56317df5a7910d5f0efa5578b7dc)\n\n\nThank you for reading this post! And again, thank you to Harpreet for the awesome [notebook](https://colab.research.google.com/drive/1q0RmeVRzLwRXW-h9dPFSOchwJkThUy6d?usp=sharing).\nIf you want to read / see more content related to AI and DS, subscribe my youtube Channel \"_The Data Journey_ \":\n![https://www.youtube.com/@TheDataJourney42](../images/011.png)\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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The first version of YOLO [was released in 201](https://arxiv.org/abs/1506.02640)6 by Joseph Redmon et. al and it smashed benchmarks both in terms of speed and accuracy. When it comes to object detection, YOLO has been a favorite of Data Scientists and Machine Learning engineers and the go-to model when it comes to segmenting entities in images.\nSince it was launched, YOLO had many new iterations that improved several setbacks of previous versions, namely:\n * Improved architecture of the underlying deep learning models.\n * Implemented alternatives to improve performance, such as data augmentation techniques.\n * Migrated the original YOLO code to use _pytorch_ training and deployment frameworks.\n * Improved detection mechanisms of small objects.\n\n\nThe last version of YOLO is YOLO v9 (). one important thing to be aware is that every computer vision and object detection model is evaluated on two parameters: **Accuracy** (defined by metrics related to computer vision segmentation) __ and **Speed** (defined by latency in the inference). One example of how CV algorithms are evaluated is shown below:\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n![Comparison of different YOLO models vs. EfficientDet - Image by https://blog.roboflow.com/yolov5-is-here/](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/0Cm7-_Xx68EE-Lre-.png)\nComparison of different YOLO models vs. EfficientDet - Image by \nIn this example, I'm going to show you how to run your own YOLO models on images and videos, so that you can rform object detection and segmentation. The entire code I'm executing is linked here, on this [Google Colab Notebook](https://colab.research.google.com/drive/1haLyNBz1i-bhh4GPizY-vsRABXs7srOQ?usp=sharing).\nPart of the code and blog post was inspired by this [great notebook](https://colab.research.google.com/drive/1q0RmeVRzLwRXW-h9dPFSOchwJkThUy6d?usp=sharing) by [Harpreet Sahota](https://datascienceharp.medium.com/).\nLet's start!\n* * *\n## Loading the Model\nWe'll start by training our own computer vision model...\nOf course not, that would be a waste of time :-)\nWe'll start by loading the pre-trained version of a large YOLO model. In this case, we'll use an implementation of YOLO with NAS ([Neural Architecture Search](https://en.wikipedia.org/wiki/Neural_architecture_search)). Neural architecture search is a common implementation of neural network optimization techniques to improve the automatic choice of parameters in the deep learning model.\nThe model we'll use was trained on the COCO dataset ([common objects in context](https://cocodataset.org/#home)). These weights already contain a very good baseline to detect objects on images.\nFirst, let's add a couple of libraries we will need to our environment, namely:\n * `torchinfo` , a helper to visualize our neural network architecture;\n * `super_gradients` , the library we will use to load our models;\n\n\ndiff\n\n```\n!pip install torchinfo\n!pip install super_gradients\n```\n\nNext, we'll load the functions and libraries we will need:\njavascript\n\n```\nfrom super_gradients.training import models\nfrom torchinfo import summary\nimport torch\n```\n\n_Note: If you are using google colab and have a prompt to restart the kernel after installing `super_gradients` , do it. It's a known issue from using `super_learners` in that environment_\nThen, we start by loading our COCO pretrained model into memory using `super_gradients` :\nini\n\n```\n# Using Yolo NAS Large\nyolo_nas_l = models.get(\"yolo_nas_l\", pretrained_weights=\"coco\")\n```\n\nCool! We have our pretrained model stored in `yolo_nas_l` . Picking up these pretrained versions of YOLO is super easy with `models` .\n`torchinfo` gives us a very cool view of our model architecture:\npython\n\n```\nsummary(model=yolo_nas_l,\n input_size=(16, 3, 640, 640),\n col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n col_width=20,\n row_settings=[\"var_names\"]\n)\n```\n\nOut of curiosity, if you would like to check the YOLO architecture (from the original paper) check out the image below:\n![Image Source: https://www.researchgate.net/publication/329038564_Complexity_and_accuracy_analysis_of_common_artificial_neural_networks_on_pedestrian_detection](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1-dSf6DJaPEaZOySkQ2Jclg.png)\nImage Source: \nHere's a preview of the architecture of our YOLO NAS model:\n![Preview of Yolo-NAS model - image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1VzypuzfWcU2R7WSoVlzo7Q.png)\nPreview of Yolo-NAS model - image by author.\nWith the YOLO weights loaded, it's time to play around with image segmentation!\n* * *\n## Segmenting Images\nLet's start with a simple image featuring a desk as the centerpiece, surrounded by various objects. In this image, we can see:\n * Two chairs\n * A desk\n * Two Lamps\n * One computer\n * Headphones\n * One plant\n\n\n![Image 1 - Image by dawidliberadzki at Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/0gQXXoV8VC5So92_2-scaled.jpg)\nImage 1 - Image by [dawidliberadzki](https://unsplash.com/pt-br/@dawidliberadzki) at Unsplash\nIn our code, we only request bounding boxes where our model has a confidence level exceeding 55%. Only labels for these items will show up in the segmented image.\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1476365518243-f738bf58443d?q=80&w=2787&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.55).show()\n```\n\n![Labeled Image with YOLO - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1wZkiUfF6AmE72ETcXO8gJg.png)\nLabeled Image with YOLO - Image by Author\nNice! Our model was able to confidently detect the chairs, laptop, desk and plant (although it missed the lamps, headphones and that hi-tech jug). Let's see what happens if we lower our confidence threshold:\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1476365518243-f738bf58443d?q=80&w=2787&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.40).show()\n```\n\n![Labeled Image with YOLO \\(40% Confidence\\) - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1v849zqgK1mJdClWZZfnmSg.png)\nLabeled Image with YOLO (40% Confidence) - Image by Author\nAh, interesting! Lowering our threshold has led to our first instance of misclassification. In this case, the headphones were incorrectly identified as a computer mouse. Reducing the threshold will inevitably result in more errors in object detection and segmentation.\nLet's try an image with a more crowded scenario:\n![Image by madebyvadim @unsplash.com](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/0efh3nZHY2VWakq6w-scaled.jpg)\nImage by [madebyvadim](https://unsplash.com/pt-br/@madebyvadim) @unsplash.com\nWhat objects will our YOLO model segment? As this image has more elements, I'll lower the confidence of the inference:\nmakefile\n\n```\nurl = \"https://images.unsplash.com/photo-1496664444929-8c75efb9546f?q=80&w=2940&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\"\nyolo_nas_l.predict(url, conf=0.3).show()\n```\n\n![Labeled Image with YOLO \\(30% Confidence\\) - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1wP2M_p7pMbZsvo1eEmR5QA.png)\nLabeled Image with YOLO (30% Confidence) - Image by Author\nAs we can see, YOLO fails to segment some of the objects - particularly smaller / thiner objects. Nevertheless, performance is quite good, particularly for such a crowded image. To improve some the performance, [transfer learning with images related](https://kikaben.com/yolov5-transfer-learning-dogs-cats/) to your use case is extremely important.\n**Now, with**`**super_gradients**`**, we're abstracting most of code that actually prepares and predicts the boundaries on our image.** If you would like to have a deeper insight on the foundations of these models, check out the article [_Training a Computer Vision Algorithm from my Pytorch Introduction Series_](https://medium.com/towards-data-science/pytorch-introduction-training-a-computer-vision-algorithm-c17ed96e64d5?sk=74bd56317df5a7910d5f0efa5578b7dc) _._\nIn the next section, we'll learn how to use YOLO-NAS, but in the context of segmenting videos.\n* * *\nFor this section, I'll be using a traffic short video on Youtube:\n![Traffic Video - https://www.youtube.com/watch?v=CftLBPI1Ga4](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1WosgRL7B4UINUniO4AAgyA.png)\nTraffic Video - \nWe'll start by downloading this video into our Google Colab environment. We can also display the video by using `IPython` 's handy `YoutubeVideo` function:\njava\n\n```\nfrom IPython.display import YouTubeVideo\n\nvideo = YouTubeVideo('CftLBPI1Ga4')\ndisplay(video)\n```\n\nTo download our video, I'll use [_youtube-dl_](https://github.com/ytdl-org/youtube-dl):\nbash\n\n```\nvideo_url = f'https://www.youtube.com/watch?v=CftLBPI1Ga4'\n\n!pip install -U \"git+https://github.com/ytdl-org/youtube-dl.git\"\n!python -m youtube_dl -f 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4' \"$video_url\"\n```\n\nWe have almost everything ready. We just need to set the output path for the segmented video and we're ready to pass our video through the YOLO model!\nini\n\n```\ninput_video_path = f\"/content/Cars, Busy Streets, City Traffic - No Copyright Royalty Free Stock Videos-CftLBPI1Ga4.mp4\"\noutput = \"traffic.mp4\"\n```\n\nAll set! Let's run our segmentation model:\nlua\n\n```\ndevice = 'cuda' if torch.cuda.is_available() else \"cpu\"\nyolo_nas_l.to(device).predict(input_video_path).save(output)\n```\n\n_Note: This code may take a bit to run, depending on the configurations of your local system and the availability of GPUs on Google Colab_\n.. and that's it! You can check out the segmented video on [my youtube channel \"The Data Journey\"](https://youtu.be/-7j4eJorJSY). Here's a cool preview:\n![Segmented Traffic Video - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1k2Pv7JAxxP1dvlrtHr80DA.png)\nSegmented Traffic Video - Image by Author\n* * *\nYOLO models are extremely powerful! You can use them to segment images or videos quite fast with just a couple lines of code. Using this code that I've shown here, it's super easy to play around with your own media and experiment with this great computer vision model.\nAs you may noticed, particularly in complex images or videos, YOLO base models still have some flaws that need to be corrected. As I've mentioned this can be improved quite well with model fine tuning. Check out this [post](https://medium.com/@yongsun.yoon/fine-tuning-yolov8-using-custom-dataset-generated-by-open-world-object-detector-5724e267645d) for more info.\nAnd, if you are curious on how the building blocks of Deep Learning work, feel free to check my _pytorch_ introduction series, that should give you a good overview on the basics of deep learning:\n * [Tensor Basics](https://towardsdatascience.com/pytorch-introduction-tensors-and-tensor-calculations-412ff818bd5b?sk=2cf4d44549664fc647baa3455e9d78e8);\n * [Create a simple linear model using Pytorch.](https://towardsdatascience.com/pytorch-introduction-building-your-first-linear-model-d868a8681a41)\n * [Learn how to work with non-linear activation functions and how to solve non-linear problems.](https://towardsdatascience.com/pytorch-introduction-enter-nonlinear-functions-4dd893845592)\n * [Using custom data.]( seen how to use custom data in the context of the library.)\n * [Training a computer vision algorithm.](https://medium.com/towards-data-science/pytorch-introduction-training-a-computer-vision-algorithm-c17ed96e64d5?sk=74bd56317df5a7910d5f0efa5578b7dc)\n\n\nThank you for reading this post! And again, thank you to Harpreet for the awesome [notebook](https://colab.research.google.com/drive/1q0RmeVRzLwRXW-h9dPFSOchwJkThUy6d?usp=sharing).\nIf you want to read / see more content related to AI and DS, subscribe my youtube Channel \"_The Data Journey_ \":\n![https://www.youtube.com/@TheDataJourney42](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1d3kWOamhkRhWNjF609t-4A.png)\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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