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href="/blog/mockoon"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #3 - Mockoon</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/dlta-ai"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #2 - DLTA-AI</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/sniffnet"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #1 - Sniffnet</span></a></li></ul></div></div></div><div class="w-full flex flex-col justify-start items-center"><div class="w-full p-4 md:p-0 mt-6 md:w-5/6 lg:max-w-6xl h-full flex flex-col justify-start items-center self-center"><img class="hidden md:block w-auto max-w-full object-scale-down" src="/assets/blog/dlta-ai/banner.webp" alt=""/><div class="w-auto max-w-6xl mt-4 md:mt-12 prose prose-indigo prose-xl md:prose-2xl flex flex-col justify-center items-center"><h1 class="leading-16">Starlet #2 DLTA-AI - Data Labeling, Tracking and Annotation with AI</h1></div><div class="w-full mt-8 mb-2 max-w-6xl px-2 flex flex-row items-center justify-center text-sm text-gray-900 font-semibold trackingwide uppercase"><div class="flex space-x-1 text-gray-500"><span class="text-gray-900">Usama Ahmed</span><span aria-hidden="true"> · </span><time dateTime="2023-07-20T00:00:00.000Z">Jul 20, 2023</time><span aria-hidden="true"> · </span><span> 5 min read </span></div></div><div class="mt-8 w-full max-w-5xl prose prose-indigo prose-xl md:prose-2xl"><p><em>This is the second issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our <a href="/blog/list-your-open-source-project">announcement</a>.</em></p> <hr> <h2>Problem</h2> <p>The annotation process is a critical component of computer vision tasks and can greatly impact the accuracy of the resulting model. However, manual annotation is a time-consuming and resource-intensive task, often requiring significant human labor. Therefore, developing an Auto Annotation tool for computer vision tasks is of utmost importance, as it has the potential to save significant time and resources while also improving the quality of the annotations and the resulting models.</p> <h2>Solution</h2> <p>The solution is to develop an automated annotation tool that can accurately and efficiently label objects in images and videos for tasks such as instance segmentation and object tracking. The tool is called <a href="https://github.com/0ssamaak0/DLTA-AI">DLTA-AI</a> (Deep Learning Tool for Annotation - Artificial Intelligence) and it has the following features:</p> <h3>Input modes</h3> <p><img src="/assets/blog/dlta-ai/input_modes.webp" alt="input modes"></p> <p>DLTA-AI supports four input modes to provide flexibility to the user depending on the application. These are: Image: load an image in the canvas to work with.</p> <p>Directory mode: load a directory of images to work with. This is useful when working with a dataset containing different images that need to be annotated together. Parallel processing is used to enhance the speed of the annotation process when applying inference using deep learning model.</p> <p>Video mode: load a video to work with. A video bar is presented below the video to allow the user to navigate through the video either by time or by frame number. The video is not loaded entirely in memory, but only the corresponding frame is loaded when needed. This allows the tool to handle large video sizes with long durations efficiently without worrying about insufficient memory.</p> <p>Video as frames: convert a chosen video into frames to work with in directory mode. The user can specify the start and end time or frame number of the video, as well as the sampling rate or time of that interval.</p> <h3>Segmentation models</h3> <p><img src="/assets/blog/dlta-ai/segmentation_models.webp" alt="segmentation models"></p> <p>DLTA-AI supports a variety of segmentation models to provide accurate and consistent annotations for objects in images and videos. These models include:</p> <p>Mmdetection: a library that contains many state-of-the-art models for instance segmentation, object detection, panoptic segmentation, and more.</p> <p>Segment anything (SAM) : a model that can segment any object in any image or video, even including objects and image types that it had not encountered during training. It uses foundation models that can perform zero-shot and few-shot learning for new datasets and tasks using prompting techniques.</p> <p>YOLOv8 family: a family of models that offer relatively fast results with reasonable accuracy.</p> <p>The user can explore and download these models using the model explorer feature that allows for easy exploration, download, and automatic path file configuration in the tool. The user can also select any model from a model selection menu in the toolba0.</p> <p>DLTA-AI also provides tuning controls for the user to adjust the segmentation parameters, such as:</p> <ul> <li>Confidence threshold: filter out detections with low confidence scores.</li> <li>IOU threshold: perform non-maximum suppression on detections with overlapping bounding boxes using intersection over union metric.</li> <li>Select classes: choose which classes to include or exclude from the segmentation results.</li> </ul> <p>Additionally, DLTA-AI allows merging multiple segmentation models together to benefit from their combined strengths and overcome their limitations. The tool runs all the chosen models in combination, then compares and merges their results based on their bounding box similarity and confidence scores².</p> <h3>Tracking algorithms</h3> <p>DLTA-AI supports multiple tracking algorithms to provide reliable and relevant annotations for objects in videos. The user can select any tracking algorithm from a tracking selection menu in the toolbar. The user can also customize the tracking process using various tracking options, such as:</p> <ul> <li>Track for certain number of frames or full video tracking: choose whether to track a specific number of frames or the entire video.</li> <li>Track selected objects: choose which objects to track from the segmentation results.</li> <li>Stop button with dynamic result saving: pause the tracking process at any time and save the results up to that point.</li> </ul> <p>DLTA-AI also incorporates interpolation methods to fill in the gaps when the detection models fail to segment certain objects or when the objects are stationary. The user can choose between linear interpolation or interpolation with segment anything model, as well as specify the key frames to interpolate between.</p> <h3>Export</h3> <p><img src="/assets/blog/dlta-ai/custom_export.webp" alt="custom export"></p> <p>DLTA-AI allows the user to export the annotations to standard formats, or any custom format such as plot, dashboard or even a report.</p> <p>In the figure above, we opened a video showing a street, sampled it by taking a frame every 3 seconds, annotated it and exported the results as this traffic graph.</p> <h2>Star Growth</h2> <p>DLTA-AI is still very young, but looks like it's been growing steadily since its open-source (April, 2023, which was only a few months ago!). Good luck, folks!</p> <p><a href="https://star-history.com/#0ssamaak0/DLTA-AI&Date"><img src="https://api.star-history.com/svg?repos=0ssamaak0/DLTA-AI&type=Date" alt="Star History Chart"></a></p> </div></div><div class="mt-12"><iframe src="https://embeds.beehiiv.com/2803dbaa-d8dd-4486-8880-4b843f3a7da6?slim=true" data-test-id="beehiiv-embed" height="52" frameBorder="0" scrolling="no" style="margin:0;border-radius:0px !important;background-color:transparent"></iframe></div></div><div class="w-full hidden lg:block"></div></div><footer class="relative w-full shrink-0 h-auto mt-6 flex flex-col justify-end items-center"><div class="w-full py-2 px-3 md:w-5/6 lg:max-w-7xl flex flex-row flex-wrap justify-between items-center text-neutral-700 border-t"><div class="text-sm leading-8 flex flex-row flex-wrap justify-start items-center"><div class="h-full text-gray-600">The missing GitHub star history graph</div><a class="h-full flex flex-row justify-center items-center ml-3 text-lg 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Ahmed","featured":true,"featureImage":"/assets/blog/dlta-ai/banner.webp","publishedDate":"2023-07-20T00:00:00.000Z","excerpt":"DLTA-AI is the next generation of annotation tools, integrating the Computer Vision SOTA models to Labelme in a seamless expirence and intuitive workflow to make creating image datasets easier than ever before.","readingTime":5},"parsedBlogHTML":"\u003cp\u003e\u003cem\u003eThis is the second issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our \u003ca href=\"/blog/list-your-open-source-project\"\u003eannouncement\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003ch2\u003eProblem\u003c/h2\u003e\n\u003cp\u003eThe annotation process is a critical component of computer vision tasks and can greatly impact the accuracy of the resulting model. However, manual annotation is a time-consuming and resource-intensive task, often requiring significant human labor. Therefore, developing an Auto Annotation tool for computer vision tasks is of utmost importance, as it has the potential to save significant time and resources while also improving the quality of the annotations and the resulting models.\u003c/p\u003e\n\u003ch2\u003eSolution\u003c/h2\u003e\n\u003cp\u003eThe solution is to develop an automated annotation tool that can accurately and efficiently label objects in images and videos for tasks such as instance segmentation and object tracking. The tool is called \u003ca href=\"https://github.com/0ssamaak0/DLTA-AI\"\u003eDLTA-AI\u003c/a\u003e (Deep Learning Tool for Annotation - Artificial Intelligence) and it has the following features:\u003c/p\u003e\n\u003ch3\u003eInput modes\u003c/h3\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/dlta-ai/input_modes.webp\" alt=\"input modes\"\u003e\u003c/p\u003e\n\u003cp\u003eDLTA-AI supports four input modes to provide flexibility to the user depending on the application. These are:\nImage: load an image in the canvas to work with.\u003c/p\u003e\n\u003cp\u003eDirectory mode: load a directory of images to work with. This is useful when working with a dataset containing different images that need to be annotated together. Parallel processing is used to enhance the speed of the annotation process when applying inference using deep learning model.\u003c/p\u003e\n\u003cp\u003eVideo mode: load a video to work with. A video bar is presented below the video to allow the user to navigate through the video either by time or by frame number. The video is not loaded entirely in memory, but only the corresponding frame is loaded when needed. This allows the tool to handle large video sizes with long durations efficiently without worrying about insufficient memory.\u003c/p\u003e\n\u003cp\u003eVideo as frames: convert a chosen video into frames to work with in directory mode. The user can specify the start and end time or frame number of the video, as well as the sampling rate or time of that interval.\u003c/p\u003e\n\u003ch3\u003eSegmentation models\u003c/h3\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/dlta-ai/segmentation_models.webp\" alt=\"segmentation models\"\u003e\u003c/p\u003e\n\u003cp\u003eDLTA-AI supports a variety of segmentation models to provide accurate and consistent annotations for objects in images and videos. These models include:\u003c/p\u003e\n\u003cp\u003eMmdetection: a library that contains many state-of-the-art models for instance segmentation, object detection, panoptic segmentation, and more.\u003c/p\u003e\n\u003cp\u003eSegment anything (SAM) : a model that can segment any object in any image or video, even including objects and image types that it had not encountered during training. It uses foundation models that can perform zero-shot and few-shot learning for new datasets and tasks using prompting techniques.\u003c/p\u003e\n\u003cp\u003eYOLOv8 family: a family of models that offer relatively fast results with reasonable accuracy.\u003c/p\u003e\n\u003cp\u003eThe user can explore and download these models using the model explorer feature that allows for easy exploration, download, and automatic path file configuration in the tool. The user can also select any model from a model selection menu in the toolba0.\u003c/p\u003e\n\u003cp\u003eDLTA-AI also provides tuning controls for the user to adjust the segmentation parameters, such as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConfidence threshold: filter out detections with low confidence scores.\u003c/li\u003e\n\u003cli\u003eIOU threshold: perform non-maximum suppression on detections with overlapping bounding boxes using intersection over union metric.\u003c/li\u003e\n\u003cli\u003eSelect classes: choose which classes to include or exclude from the segmentation results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditionally, DLTA-AI allows merging multiple segmentation models together to benefit from their combined strengths and overcome their limitations. The tool runs all the chosen models in combination, then compares and merges their results based on their bounding box similarity and confidence scores².\u003c/p\u003e\n\u003ch3\u003eTracking algorithms\u003c/h3\u003e\n\u003cp\u003eDLTA-AI supports multiple tracking algorithms to provide reliable and relevant annotations for objects in videos.\nThe user can select any tracking algorithm from a tracking selection menu in the toolbar. The user can also customize the tracking process using various tracking options, such as:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTrack for certain number of frames or full video tracking: choose whether to track a specific number of frames or the entire video.\u003c/li\u003e\n\u003cli\u003eTrack selected objects: choose which objects to track from the segmentation results.\u003c/li\u003e\n\u003cli\u003eStop button with dynamic result saving: pause the tracking process at any time and save the results up to that point.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDLTA-AI also incorporates interpolation methods to fill in the gaps when the detection models fail to segment certain objects or when the objects are stationary. The user can choose between linear interpolation or interpolation with segment anything model, as well as specify the key frames to interpolate between.\u003c/p\u003e\n\u003ch3\u003eExport\u003c/h3\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/dlta-ai/custom_export.webp\" alt=\"custom export\"\u003e\u003c/p\u003e\n\u003cp\u003eDLTA-AI allows the user to export the annotations to standard formats, or any custom format such as plot, dashboard or even a report.\u003c/p\u003e\n\u003cp\u003eIn the figure above, we opened a video showing a street, sampled it by taking a frame every 3 seconds, annotated it and exported the results as this traffic graph.\u003c/p\u003e\n\u003ch2\u003eStar Growth\u003c/h2\u003e\n\u003cp\u003eDLTA-AI is still very young, but looks like it\u0026#39;s been growing steadily since its open-source (April, 2023, which was only a few months ago!). Good luck, folks!\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://star-history.com/#0ssamaak0/DLTA-AI\u0026Date\"\u003e\u003cimg src=\"https://api.star-history.com/svg?repos=0ssamaak0/DLTA-AI\u0026type=Date\" alt=\"Star History Chart\"\u003e\u003c/a\u003e\u003c/p\u003e\n"},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"dlta-ai"},"buildId":"xKX4ZiOi_N7h3OBOEsSZu","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>