a remote security camera), issue: After a few moments of initializing, a window will appear showing the video stream. First, we’ll run the model through TOCO to create an optimzed TensorFLow Lite model. As I mentioned before, you can build either the CPU-only version of TensorFlow or the GPU-enabled version of TensorFlow. It also automatically converts Windows-style directory paths to Linux-style paths when using Bazel. The build process took about 70 minutes on my computer. I'll show two options for setting up an Edge TPU model: using the sample model from Google, or using a custom model you compiled yourself. Open File Explorer and browse to the C:\tmp\tensorflow_pkg folder. Insert picture of Coral USB Accelerator plugged into Raspberry Pi here! Here’s how you can check the version of TensorFlow you used for training. It occurs because the package data got corrupted while downloading. Line 181. Today we try to optimize an object detection model and improve performance with TensorFlow Lite. The main differences are the following. The code in this repository is written for object detection models. Detected objects will have bounding boxes and labels displayed on them in real time. This error occurs when trying to use a newer version of the libedgetpu library (v13.0 or greater) with an older version of TensorFlow (v2.0 or older). Next, we’ll install Bazel and some other Python packages that are used for building TensorFlow. Also, the paths must be in double quotation marks ( " ), not single quotation marks ( ' ). Google has provided a list of build configurations for Linux, macOS, and Windows that show which tool versions were used to build and run each version of TensorFlow. In the Anaconda Prompt window, issue these two commands: The update process may take up to an hour, depending on how it's been since you installed or updated Anaconda. Using model_main.py requires a few extra setup steps, and I want to keep this guide as simple as possible. Make sure to use python3 rather than python when running the scripts. At the end of the instructions, there is a sample python script for face detection with OpenCV and the pre-trained face detection model. See the FAQs section for instructions on how to check the TensorFlow version you used for training. Now that those are set up, issue this command to export the model for TensorFlow Lite: After the command has executed, there should be two new files in the \object_detection\TFLite_model folder: tflite_graph.pb and tflite_graph.pbtxt. However, it didn't seem hot enough to be unsafe or harmful to the electronics. This page describes how to build the TensorFlow Lite static and shared libraries for Raspberry Pi. If your model folder has a different name than "Sample_TFLite_model", use that name instead. Set up TensorFlow Lite detection model. Google TensorFlow 1.9 officially supports the Raspberry Pi, making it possible to quickly install TensorFlow and start learning AI techniques with a Raspberry Pi. If you’re using an SSD-MobileNet model that has already been trained, you can skip to Step 1d of this guide. Now, close the MSYS2 window. The 'NoneType' error means that the program received an empty array from the webcam, which typically means something is wrong with the webcam or the interface to the webcam. Deploy a TensorFlow Lite object detection model (MobileNetV3-SSD) to a Raspberry Pi. We'll use Anaconda's git package to download the TensorFlow repository, so install git using: Next, add the MSYS2 binaries to this environment's PATH variable by issuing: (If MSYS2 is installed in a different location than C:\msys64, use that location instead.) It follows the Build TensorFlow From Source on Windows instructions given on the official TensorFlow website, with some slight modifications. Once training is complete (i.e. I used TensorFlow v1.13 while creating this guide, because TF v1.13 is a stable version that has great support from Anaconda. Here we need TensorFlow, Object Detection API, Pre-trained object detection model, OpenCV, Protobuf, and some other dependencies in this project. First, we have to build TensorFlow from source. Basically, press Enter to select the default option for each question. It also shows how to set up the Coral USB Accelerator on the Pi and run Edge TPU detection models. Accelerate inferences of any TensorFlow Lite model with Coral’s USB Edge TPU Accelerator and Edge TPU Compiler. It only supports SSD models. Download and install the following two packages: Restart your PC after installation has finished. How to Run TensorFlow Lite Object Detection Models on the Raspberry Pi (with optional Coral USB Accelerator), https://github.com/tensorflow/tensorflow/issues/15925#issuecomment-499569928, How to Train, Convert, and Run Custom TensorFlow Lite Object Detection Models on Windows 10, How to Run TensorFlow Lite Object Detection Models on Android Devices For running models on edge devices and mobile-phones, it's recommended to convert the model to Tensorflow Lite. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. Make sure you have a webcam plugged in. Check the TFLite Python Quickstart page for download URLs to the latest version of tflite_runtime. For my bird/squirrel/raccoon detector example, there are 582 test images, so I set num_examples: 582. Here's a guide on adding vision and machine learning using Tensorflow Lite on the Raspberry Pi 4. Here’s what the configuration session will look like if you are building for CPU only. This guide is the second part of my larger TensorFlow Lite tutorial series: TensorFlow Lite (TFLite) models run much faster than regular TensorFlow models on the Raspberry Pi. Open a text editor and list each class in order of their class number. There are three primary steps to training and deploying a TensorFlow Lite model: This portion is a continuation of my previous guide: How To Train an Object Detection Model Using TensorFlow on Windows 10. To run the image detection script, issue: The image will appear with all objects labeled. The scripts are based off the label_image.py example given in the TensorFlow Lite examples GitHub repository. Raspberry pi 4 TensorFlow-lite Video Object Detection ... How To Run TensorFlow Lite on Raspberry Pi for Object Detection - Duration: 10:48. Now that the Visual Studio tools are installed and your PC is freshly restarted, open a new Anaconda Prompt window. For example, if you've already installed TensorFlow v1.8 on the Pi using my other guide, you can leave that installation as-is without having to worry about overriding it. Install Microsoft Build Tools 2015 and Microsoft Visual C++ 2015 Redistributable by visiting the Visual Studio older downloads page. Then, create the "tflite1-env" virtual environment by issuing: This will create a folder called tflite1-env inside the tflite1 directory. Editor’s note: This post is part of our Trainspotting series, a deep dive into the visual and audio detection components of our Caltrain project. Now that the libedgetpu runtime is installed, it's time to set up an Edge TPU detection model to use it with. Edje Electronics 243,286 views. If the bounding boxes are not matching the detected objects, probably the stream resolution wasn't detected. Edge TPU models are TensorFlow Lite models that have been compiled specifically to run on Edge TPU devices like the Coral USB Accelerator. If you’d like to build the GPU-enabled version anyway, then you need to have the appropriate version of CUDA and cuDNN installed. For example: Make sure you have a USB webcam plugged into your computer. This guide provides step-by-step instructions for how to set up TensorFlow Lite on the Raspberry Pi and use it to run object detection models. TensorFlow Lite vs Tensorflow. From the C:\tensorflow-build\tensorflow directory, issue: This will initiate a Bazel session. Raspberry Pi, TensorFlow Lite and Qt: object detection app. To open a specific video file, use the --video option: Note: Video detection will run at a slower FPS than realtime webcam detection. I'll show the steps needed to train, convert, and run a quantized TensorFlow Lite version of the bird/squirrel/raccoon detector. If you're not feeling up to training and converting your own TensorFlow Lite model, you can skip Part 1 and use my custom-trained TFLite BSR detection model (which you can download from Dropbox here) or use the TF Lite starter detection model (taken from https://www.tensorflow.org/lite/models/object_detection/overview) for Part 2 or Part 3. Quantized models use 8-bit integer values instead of 32-bit floating values within the neural network, allowing them to run much more efficiently on GPUs or specialized TPUs (TensorFlow Processing Units). We previously used Raspberry Pi for other Image Processing tasks like Optical Character Recognition , Face Recognition , Number Plate Detection … It makes object detection models run WAY faster, and it's easy to set up. Or vice versa. These are the steps needed to set up TensorFlow Lite: I also made a YouTube video that walks through this guide: First, the Raspberry Pi needs to be fully updated. Unfortunately, the edgetpu-compiler package doesn't work on the Raspberry Pi: you need a Linux PC to use it on. Also, make sure you have your webcam or Picamera plugged in. Make the following changes to the ssd_mobilenet_v2_quantized_300x300_coco.config file. Assuming you've been able to compile your TFLite model into an EdgeTPU model, you can simply copy the .tflite file onto a USB and transfer it to the model folder on your Raspberry Pi. If you install the -max library, the -std library will automatically be uninstalled.). Subscribe to Newsletter. This tutorial will use the SSD-MobileNet-V2-Quantized-COCO model. Change label_map_path to: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt". Run TensorFlow Lite Object Detection Models on the Raspberry Pi, Section 2. Now that the package builder has been created, let’s use it to build the actual TensorFlow wheel file. Accelerating inferences of any TensorFlow Lite model with Coral's USB Edge TPU Accelerator and Edge TPU Compiler. This guide uses "train.py" to run training on the TFLite detection model. If you encounter errors while running these scripts, please check the FAQ section of this guide. However, it also causes the USB Accelerator to get hotter. The extreme paralellization and removal of the memory bottleneck means the TPU can perform up to 4 trillion arithmetic operations per second! Here are the framerates I get when running TFLite_detection_webcam.py with 1280x720 resolution for each option with a Raspberry Pi 4 4GB model: I didn't measure the temperature of the USB Accelerator, but it does get a little hotter to the touch with the libedgetpu1-max version. The Coral USB Accelerator is a USB hardware accessory for speeding up TensorFlow models. The detection will run SIGNIFICANTLY faster with the Coral USB Accelerator. Raspberry Pi 4 Model B - 4 GB RAM Issue these commands (make sure you are inside the tflite1-env virtual environment): (Or, if you're using Python 3.5, use pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp35-cp35m-linux_armv7l.whl instead.). The easiest way to compile the Edge TPU model is to use a Google Colab session. Thus, we need to create a new label map that matches the TensorFlow Lite style. Want to up your robotics game and give it the ability to detect objects? If you'd like to train your own model to detect custom objects, you'll also need to work through Steps 3, 4, and 5. Learn more. (Before running the command, make sure the tflite1-env environment is active by checking that (tflite1-env) appears in front of the command prompt.) The source code of this example app is open source and it is hosted in our GitHub account. To open a specific image file, use the --image option: It can also open an entire folder full of images and perform detection on each image. These tutorial combined from EdjeElectronics article how to build model and run… Edge TPU – Raspberry Pi object detection app This app is open source and it is hosted in a Git repository on GitHub. Download the sample model (which can be found on the Object Detection page of the official TensorFlow website) by issuing: Unzip it to a folder called "Sample_TFLite_model" by issuing (this command automatically creates the folder): Okay, the sample model is all ready to go! If you want to use the libedgetpu-max library, install it by using sudo apt-get install libedgetpu1-max. (You can't have both the -std and the -max libraries installed. I'll assume you have already set up TensorFlow to train a custom object detection model as described in that guide, including: This tutorial uses the same Anaconda virtual environment, files, and directory structure that was set up in the previous one. Change num_examples to the number of images you have in the \images\test directory. After the file has been fully unzipped, you should have a folder called "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03" within the \object_detection folder. When I run the code the pin does go HIGH on a perfect detection but stays HIGH even if I remove the object from webcam feed. On to the last step: Step 3! Next, clone this GitHub repository by issuing the following command. The TOCO tool lives deep in the C:\tensorflow-build directory, and it will be run from the “tensorflow-build” Anaconda virtual environment that we created and used during Step 2. You can find the introduction to the series here.. SVDS has previously used real-time, publicly available data to improve Caltrain arrival predictions. Download the msys2-x86_64 executable file and run it. TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi, download the GitHub extension for Visual Studio, Import from tflite_runtime over tensorflow, How to Run TensorFlow Lite Object Detection Models on the Raspberry Pi (with optional Coral USB Accelerator), tutorial in the TensorFlow Object Detection repository, Train a quantized SSD-MobileNet model using TensorFlow, and export frozen graph for TensorFlow Lite, Use TensorFlow Lite Optimizing Converter (TOCO) to create optimzed TensorFlow Lite model, How To Train an Object Detection Model Using TensorFlow on Windows 10, (which you can download from Dropbox here), https://www.tensorflow.org/lite/models/object_detection/overview, Steps 2, 3, 4, and 5 of my previous tutorial, example TensorFlow Lite object detection model, TensorFlow Lite examples GitHub repository, Part 2. Back in The MagPi issue 71 we noted that it was getting easier to install TensorFlow on a Raspberry Pi. The next two parts of my guide show how to run this TFLite model on a Raspberry Pi or an Android Device. This is perfect for running deep neural networks, which require millions of multiply-accumulate operations to generate outputs from a single batch of input data. (It will work on Linux too with some minor changes, which I leave as an exercise for the Linux user.). If nothing happens, download GitHub Desktop and try again. Once the edgetpu.tflite file has been moved into the model folder, it's ready to go! Note: the URLs provided in these commands may change as newer versions of tflite_runtime are released. Create and activate the environment by issuing: After the environment is activated, you should see (tensorflow-build) before the active path in the command window. If you get an error, try re-running the command a few more times. If you’d still like to build the GPU-enabled version for some other reason, then you need to have the appropriate version of CUDA and cuDNN installed. Go grab a cup of coffee while it's working! The TensorFlow team is always hard at work releasing updated versions of TensorFlow. The tflite1-env folder will hold all the package libraries for this environment. (Still not complete), Setting up an Anaconda virtual environment for training, Setting up TensorFlow directory structure, Preparing training data (generating TFRecords and label map), Train and test images and their XML label files are placed in the \object_detection\images\train and \object_detection\images\test folders, train_labels.csv and test_labels.csv have been generated and are located in the \object_detection\images folder, train.record and test.record have been generated and are located in the \object_detection folder, labelmap.pbtxt file has been created and is located in the \object_detection\training folder, proto files in \object_detection\protos have been generated, Part 3. By default, the image detection script will open an image named 'test1.jpg'. It works for the Raspberry Pi 3 and Raspberry Pi 4 running either Rasbpian Buster or Rasbpian Stretch. First, move the “train.py” file from the \object_detection\legacy folder into the main \object_detection folder. First, we’ll use transfer learning to train a “quantized” SSD-MobileNet model. Download it and move it into the Sample_TFLite_model folder (while simultaneously renaming it to "edgetpu.tflite") by issuing these commands: Now the sample Edge TPU model is all ready to go. Check the build configuration list to see which versions of CUDA and cuDNN are compatible with which versions of TensorFlow. If you’d like try using the sample TFLite object detection model provided by Google, simply download it here and unzip it into the \object_detection folder. I removed the ' characters from the command, because for some reason they cause errors on Windows! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. On to Step 2! Deploying a TensorFlow Lite object-detection model (MobileNetV3-SSD) to a Raspberry Pi. The app is mostly the same as the one developed in Raspberry Pi, TensorFlow Lite and Qt/QML: object detection example. You can resolve the error by re-running the bash get_pi_requirements.sh command a few more times until it successfully completes without reporting that error. Part 1 of this guide gives instructions for training and deploying your own custom TensorFlow Lite object detection model on a Windows 10 PC. Note: TensorFlow Lite does NOT support RCNN models such as Faster-RCNN! A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! The new inference graph has been trained and exported. Then, use --modeldir=coco_ssd_mobilenet_v1_1.0_quant_2018_06_29 rather than --modeldir=TFLite_model when running the script. https://colab.research.google.com/drive/1o6cNNNgGhoT7_DR4jhpMKpq3mZZ6Of4N?usp=sharing. Feel free to create Pull Requests to add your own errors and resolutions! Since there are no major differences between train.py and model_main.py that will affect training (see TensorFlow Issue #6100), I use train.py for this guide. . All that's left to do is train the model! Line 189. If you just want to start using TensorFlow Lite to execute your models, the fastest option is to install the TensorFlow Lite runtime package as shown in the Python quickstart.. Then, save the file as “labelmap.txt” in the TFLite_model folder. To make things easier, I wrote a shell script that will automatically download and install all the packages and dependencies. A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more! That's a little long to work with, so rename the folder to "tflite1" and then cd into it: We'll work in this /home/pi/tflite1 directory for the rest of the guide. That’s it! Many people run in to this error when using models from Teachable Machine. If you'd like to see everything that gets installed, simply open get_pi_dependencies.sh to view the list of packages. This error occurs when you try to run any of the TFLite_detection scripts without activating the 'tflite1-env' first. Alright! Although we've already exported a frozen graph of our detection model for TensorFlow Lite, we still need run it through the TensorFlow Lite Optimizing Converter (TOCO) before it will work with the TensorFlow Lite interpreter. Take note of the checkpoint number of the model.ckpt file in the training folder (i.e. This guide shows how to either download a sample TFLite model provided by Google, or how to use a model that you've trained yourself by following Part 1 of my TensorFlow Lite tutorial series. Part 2 - How to Run TensorFlow Lite Object Detection Models on the Raspberry Pi (with Optional Coral USB Accelerator) Introduction. For example, I would use --modeldir=BirdSquirrelRaccoon_TFLite_model to run my custom bird, squirrel, and raccoon detection model. Download this repository and create virtual environment, 1d. Make sure to update the URL parameter to the one that's being used by your security camera. This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on Android phones or the Raspberry Pi. I created a Colab page specifically for compiling Edge TPU models. OpenCV is not needed to run TensorFlow Lite, but the object detection scripts in this repository use it to grab images and draw detection results on them. Now that training has finished, the model can be exported for conversion to TensorFlow Lite using the export_tflite_ssd_graph.py script. It gives solutions to common errors that occur. The TensorFlow installation guide explains how to install CUDA and cuDNN. My Master's degree was in ASIC design, so the Edge TPU is very interesting to me! For our experiment, we had chosen the following models: tiny YOLO and SSD MobileNet lite. The FAQ has further discussion on how to resolve this. The classic TensorFlow label map format looks like this (you can see an example in the \object_detection\data\mscoco_label_map.pbtxt file): However, the label map provided with the example TensorFlow Lite object detection model looks like this: Basically, rather than explicitly stating the name and ID number for each class like the classic TensorFlow label map format does, the TensorFlow Lite format just lists each class. Part 3 of my TensorFlow Lite training guide gives instructions for using the TFLite_detection_image.py and TFLite_detection_video.py scripts. My preferred way to organize the model files is to create a folder (such as "BirdSquirrelRaccoon_TFLite_model") and keep both the detect.tflite and labelmap.txt in that folder. It will use the same labelmap.txt file that already exists in the folder to get its labels. Note: This page shows how to compile the C++ static and shared libraries for TensorFlow Lite. the ssdlite_mobilenet_v2_coco model rather than the ssd_mobilenet_v2_quantized_coco model), the Bazel TOCO command must be modified slightly: If you are using Linux, make sure to use the commands given in the official TensorFlow instructions here. I only have one object/class in my trained model, when it is detected I want to make a gpio pin to go HIGH (LED) and when the object is removed from the webcam feed the pin should go LOW. From the \object_detection directory, issue: After a few moments of initializing, a window will appear showing the webcam feed. Next, we’ll configure the TensorFlow build using the configure.py script. For more information on options that can be used while running the scripts, use the -h option when calling the script. 2. You really need a Pi 4 or better, TensorFlow vision recognition will not run on anything slower! It can detect and identify 80 different common objects, such as people, cars, cups, etc. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. Here's what your \object_detection folder should look like: If you don't have this folder, please go to my previous tutorial and work through at least Steps 1 and 2. After a few moments of initializing, a window will appear showing the webcam feed. (Henceforth, this folder will be referred to as the “\object_detection” folder.) Run it by issuing: This downloads about 400MB worth of installation files, so it will take a while. Press 'q' to close the image and end the script. Work fast with our official CLI. If your directory looks good, it's time to move on to Step 1c! TensorFlow is installed! The Bazel build won’t work without MSYS2 installed! TensorFlow Lite is the official solution for running machine learning models on mobile and embedded devices. If you haven't done that portion, scroll back up and work through it first. This part of the tutorial breaks down step-by-step how to build TensorFlow from source on your Windows PC. After the command finishes running, you should see a file called detect.tflite in the \object_detection\TFLite_model directory. Then, run the real-time webcam detection script with the --edgetpu argument: The --edgetpu argument tells the script to use the Coral USB Accelerator and the EdgeTPU-compiled .tflite file. Change batch_size: 24 to batch_size: 6 . While either will work, object detection runs much faster on the Pi 4, as it has a faster processor and more memory. Do not use both the --image option and the --imagedir option when running the script, or it will throw an error. NOTE: If you get an error while running the bash get_pi_requirements.sh command, it's likely because your internet connection timed out, or because the downloaded package data was corrupted. Hi @thienuittc . Then, open a new Anaconda Prompt window by searching for “Anaconda Prompt” in the Start menu and clicking on it. This guide doesn't cover building the GPU-enabled version of TensorFlow, but you can try following the official build instructions on the TensorFlow website. The intent of this detection model is to watch a bird feeder, and record videos of birds while triggering an alarm if a squirrel or raccoon is stealing from it! You signed in with another tab or window. Try plugging and re-plugging the webcam in a few times, and/or power cycling the Raspberry Pi, and see if that works. Then, open the file using a text editor. Click the “Redistributables and Build Tools” dropdown at the bottom of the list. For my bird/squirrel/raccoon detector example, there are three classes, so I set num_classes: 3. You can tell when the environment is active by checking if (tflite1-env) appears before the path in your command prompt, as shown in the screenshot below. Be replaced with the Coral USB Accelerator, section 3 ( note, edgetpu-compiler. People run in to one of the USB ports on the Raspberry Pi and it. Using an SSD-MobileNet model gives instructions for training. ) tensorflow lite object detection raspberry pi directory only building.... Gpu are that works the -- image option and the scripts libararies for computer vision detection and classification including TensorFlow! 'S working the bash get_pi_requirements.sh command a few extra setup steps, and only on certain CPU architectures object... Python ” Lite examples GitHub repository open file Explorer and browse to the number of command! The Pi and run Edge TPU – Raspberry Pi ( with Optional Coral USB Accelerator one! 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