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Gabriel Rh
Gabriel Rh

AndroidYolo: A Guide to Real-Time Object Detection with TensorFlow


Androidyolo: What is it and how to use it?




Have you ever wondered what objects are in an image or a video? Do you want to detect and recognize objects in real-time on your Android device? If yes, then you might be interested in androidyolo, a project that allows you to run YOLO, a state-of-the-art object detection algorithm, on your Android device. In this article, we will explain what androidyolo is, what YOLO is, why you should use androidyolo, and how to use it. Let's get started!




androidyolo



Introduction




What is androidyolo?




Androidyolo is the first implementation of YOLO for TensorFlow on an Android device. It is compatible with Android Studio and usable out of the box. It can detect the 20 classes of objects in the Pascal VOC dataset: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train and tv/monitor. The network only outputs one predicted bounding box at a time for now. The code can and will be extended in the future to output several predictions.


What is YOLO?




YOLO stands for You Only Look Once. It is a deep learning algorithm that can detect and classify objects in images and videos. Unlike other object detection methods that use regions or sliding windows to locate objects, YOLO divides the input image into a grid of cells and predicts bounding boxes and probabilities for each cell. This makes YOLO fast and accurate, as it can process images in real-time with high accuracy.


Why use androidyolo?




Androidyolo has several advantages over other object detection apps. First of all, it is open-source and free to use. You can download the source code from GitHub and modify it as you wish. Second, it uses TensorFlow, a popular and powerful framework for machine learning. TensorFlow allows you to run complex computations on your device without relying on cloud services or internet connection. Third, it uses YOLO, which is one of the best object detection algorithms available. YOLO can detect multiple objects in an image with high precision and speed.


How to use androidyolo?




Download and install androidyolo




From GitHub




If you want to download the source code of androidyolo and build it yourself, you can follow these steps:


Clone the repository from GitHub using this command: git clone


  • Download the TensorFlow YOLO model from this link: [6]( and put it in android-yolo/app/src/main/assets



  • Open the project on Android Studio



  • Run the project on your Android device using the Run 'app' command and selecting your device



From APK file




If you just want to try out androidyolo without building it yourself, you can download the standalone APK file from this link: [7]( Just open your browser on your Android device and download the APK file. When the file has been downloaded it should begin installing on your device after you grant the required permissions.</p Run androidyolo on your device




Grant permissions




Before you can use androidyolo, you need to grant some permissions to the app. The app will ask for permission to access your camera and your storage. These permissions are necessary for the app to capture images and videos from your device and to save the results. To grant the permissions, just tap on Allow when prompted.


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Choose camera or gallery




Once you have granted the permissions, you can choose whether you want to use the camera or the gallery mode. The camera mode allows you to detect objects in real-time using your device's camera. The gallery mode allows you to detect objects in images or videos that are already stored on your device. To switch between the modes, just tap on the icon at the top right corner of the screen.


See the results




When you are in the camera mode, you can see the objects that are detected by androidyolo on your screen. The app will draw a bounding box around each object and label it with its name and confidence score. You can also see the FPS (frames per second) and inference time at the bottom of the screen. To capture an image or a video, just tap on the shutter button at the bottom center of the screen. The app will save the image or video on your device and show you a preview of it.


When you are in the gallery mode, you can select an image or a video from your device's gallery by tapping on the icon at the bottom left corner of the screen. The app will then process the image or video and show you the objects that are detected by androidyolo. You can see the same information as in the camera mode, such as bounding boxes, labels, confidence scores, FPS, and inference time. You can also save the results by tapping on the icon at the bottom right corner of the screen.


Conclusion




Summary of the article




In this article, we have learned what androidyolo is, what YOLO is, why we should use androidyolo, and how to use it. We have seen that androidyolo is a project that allows us to run YOLO, a fast and accurate object detection algorithm, on our Android devices using TensorFlow. We have also learned how to download and install androidyolo from GitHub or APK file, and how to run it on our devices using camera or gallery mode. We have seen how androidyolo can detect and recognize objects in images and videos with high precision and speed.


FAQs




  • Q: What are the requirements for running androidyolo?



  • A: You need an Android device with API level 21 or higher, and at least 1 GB of RAM.



  • Q: How can I change the settings of androidyolo?



  • A: You can access the settings menu by tapping on the icon at the top left corner of the screen. You can change various parameters such as input size, confidence threshold, non-max suppression threshold, number of threads, and GPU usage.



  • Q: How can I improve the performance of androidyolo?



  • A: You can try to lower the input size, increase the confidence threshold, decrease the non-max suppression threshold, increase the number of threads, and enable GPU usage. However, these changes may affect the accuracy of the results.



  • Q: How can I contribute to androidyolo?



  • A: You can fork the repository from GitHub and make your own modifications. You can also report issues or suggest features on GitHub.



  • Q: Where can I find more information about androidyolo?



  • A: You can visit [5]( for more details about androidyolo. You can also visit [8]( for more information about YOLO.





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