From a2cb76c9b2ac2a2c2086c4e127535e190d10d4ba Mon Sep 17 00:00:00 2001 From: Marcelo Rovai Date: Fri, 6 Oct 2023 10:37:29 -0300 Subject: [PATCH] Add files via upload --- embedded_ml_exercise.qmd | 747 +++++++++++++++++++++++++++++++++++++ images_4/media/image1.jpg | Bin 0 -> 8426 bytes images_4/media/image10.jpg | Bin 0 -> 24866 bytes images_4/media/image11.png | Bin 0 -> 37841 bytes images_4/media/image12.png | Bin 0 -> 40783 bytes images_4/media/image13.jpg | Bin 0 -> 23868 bytes images_4/media/image14.png | Bin 0 -> 64935 bytes images_4/media/image15.jpg | Bin 0 -> 32737 bytes images_4/media/image16.png | Bin 0 -> 37603 bytes images_4/media/image17.png | Bin 0 -> 39156 bytes images_4/media/image18.jpg | Bin 0 -> 65664 bytes images_4/media/image19.jpg | Bin 0 -> 41254 bytes images_4/media/image2.jpg | Bin 0 -> 28343 bytes images_4/media/image20.png | Bin 0 -> 36980 bytes images_4/media/image21.jpg | Bin 0 -> 33582 bytes images_4/media/image22.png | Bin 0 -> 22318 bytes 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embedded_ml_exercise.qmd new file mode 100644 index 00000000..4d3673e3 --- /dev/null +++ b/ embedded_ml_exercise.qmd @@ -0,0 +1,747 @@ +4 Embedded AI + +# Exercise - Image Classification + +### **Introduction** + +As we initiate our studies into embedded machine learning or tinyML, +it\'s impossible to overlook the transformative impact of Computer +Vision (CV) and Artificial Intelligence (AI) in our lives. These two +intertwined disciplines redefine what machines can perceive and +accomplish, from autonomous vehicles and robotics to healthcare and +surveillance. + +More and more, we are facing an artificial intelligence (AI) revolution +where, as stated by Gartner, **Edge AI** has a very high impact +potential, and **it is for now**! + +![](images_4/media/image2.jpg){width="4.729166666666667in" +height="4.895833333333333in"} + +In the \"bull-eye\" of emerging technologies, radar is the *Edge +Computer Vision*, and when we talk about Machine Learning (ML) applied +to vision, the first thing that comes to mind is **Image +Classification**, a kind of ML \"Hello World\"! + +This exercise will explore a computer vision project utilizing +Convolutional Neural Networks (CNNs) for real-time image classification. +Leveraging TensorFlow\'s robust ecosystem, we\'ll implement a +pre-trained MobileNet model and adapt it for edge deployment. The focus +will be optimizing the model to run efficiently on resource-constrained +hardware without sacrificing accuracy. + +We\'ll employ techniques like quantization and pruning to reduce the +computational load. By the end of this tutorial, you\'ll have a working +prototype capable of classifying images in real time, all running on a +low-power embedded system based on the Arduino Nicla Vision board. + +### **Computer Vision** + +At its core, computer vision aims to enable machines to interpret and +make decisions based on visual data from the world---essentially +mimicking the capability of the human optical system. Conversely, AI is +a broader field encompassing machine learning, natural language +processing, and robotics, among other technologies. When you bring AI +algorithms into computer vision projects, you supercharge the system\'s +ability to understand, interpret, and react to visual stimuli. + +When discussing Computer Vision projects applied to embedded devices, +the most common applications that come to mind are *Image +Classification* and *Object Detection*. + +![image.png](images_4/media/image15.jpg){width="6.5in" +height="2.8333333333333335in"} + +Both models can be implemented on tiny devices like the Arduino Nicla +Vision and used on real projects. Let\'s start with the first one. + +### **Image Classification Project** + +The first step in any ML project is to define our goal. In this case, it +is to detect and classify two specific objects present in one image. For +this project, we will use two small toys: a *robot* and a small +Brazilian parrot (named *Periquito*). Also, we will collect images of a +*background* where those two objects are absent. + +![image.png](images_4/media/image36.jpg){width="6.5in" +height="3.638888888888889in"} + +### **Data Collection** + +Once you have defined your Machine Learning project goal, the next and +most crucial step is the dataset collection. You can use the Edge +Impulse Studio, the OpenMV IDE we installed, or even your phone for the +image capture. Here, we will use the OpenMV IDE for that. + +**Collecting Dataset with OpenMV IDE** + +First, create in your computer a folder where your data will be saved, +for example, \"data.\" Next, on the OpenMV IDE, go to Tools \> Dataset +Editor and select New Dataset to start the dataset collection: + +![image.png](images_4/media/image29.png){width="6.291666666666667in" +height="4.010416666666667in"} + +The IDE will ask you to open the file where your data will be saved and +choose the \"data\" folder that was created. Note that new icons will +appear on the Left panel. + +![image.png](images_4/media/image46.png){width="0.9583333333333334in" +height="1.5208333333333333in"} + +Using the upper icon (1), enter with the first class name, for example, +\"periquito\": + +![image.png](images_4/media/image22.png){width="3.25in" +height="2.65625in"} + +Run the dataset_capture_script.py, and clicking on the bottom icon (2), +will start capturing images: + +![image.png](images_4/media/image43.png){width="6.5in" +height="4.041666666666667in"} + +Repeat the same procedure with the other classes + +![image.png](images_4/media/image6.jpg){width="6.5in" +height="3.0972222222222223in"} + +> *We suggest around 60 images from each category. Try to capture +> different angles, backgrounds, and light conditions.* + +The stored images use a QVGA frame size 320x240 and RGB565 (color pixel +format). + +After capturing your dataset, close the Dataset Editor Tool on the Tools +\> Dataset Editor. + +On your computer, you will end with a dataset that contains three +classes: periquito, robot, and background. + +![image.png](images_4/media/image20.png){width="6.5in" +height="2.2083333333333335in"} + +You should return to Edge Impulse Studio and upload the dataset to your +project. + +### **Training the model with Edge Impulse Studio** + +We will use the Edge Impulse Studio for training our model. Enter your +account credentials at Edge Impulse and create a new project: + +![image.png](images_4/media/image45.png){width="6.5in" +height="4.263888888888889in"} + +> *Here, you can clone a similar project:* +> *[NICLA-Vision_Image_Classification](https://studio.edgeimpulse.com/public/273858/latest).* + +### **Dataset** + +Using the EI Studio (or *Studio*), we will pass over four main steps to +have our model ready for use on the Nicla Vision board: Dataset, +Impulse, Tests, and Deploy (on the Edge Device, in this case, the +NiclaV). + +![image.png](images_4/media/image41.jpg){width="6.5in" +height="4.194444444444445in"} + +Regarding the Dataset, it is essential to point out that our Original +Dataset, captured with the OpenMV IDE, will be split into three parts: +Training, Validation, and Test. The Test Set will be divided from the +beginning and left a part to be used only in the Test phase after +training. The Validation Set will be used during training. + +![image.png](images_4/media/image7.jpg){width="6.5in" +height="4.763888888888889in"} + +On Studio, go to the Data acquisition tab, and on the UPLOAD DATA +section, upload from your computer the files from chosen categories: + +![image.png](images_4/media/image39.png){width="6.5in" +height="4.263888888888889in"} + +Left to the Studio to automatically split the original dataset into +training and test and choose the label related to that specific data: + +![image.png](images_4/media/image30.png){width="6.5in" +height="4.263888888888889in"} + +Repeat the procedure for all three classes. At the end, you should see +your \"raw data in the Studio: + +![image.png](images_4/media/image11.png){width="6.5in" +height="4.263888888888889in"} + +The Studio allows you to explore your data, showing a complete view of +all the data in your project. You can clear, inspect, or change labels +by clicking on individual data items. In our case, a simple project, the +data seems OK. + +![image.png](images_4/media/image44.png){width="6.5in" +height="4.263888888888889in"} + +### **The Impulse Design** + +In this phase, we should define how to: + +- Pre-process our data, which consists of resizing the individual + > images and determining the color depth to use (RGB or Grayscale) + > and + +- Design a Model that will be \"Transfer Learning (Images)\" to + > fine-tune a pre-trained MobileNet V2 image classification model on + > our data. This method performs well even with relatively small + > image datasets (around 150 images in our case). + +![image.png](images_4/media/image23.jpg){width="6.5in" +height="4.0in"} + +Transfer Learning with MobileNet offers a streamlined approach to model +training, which is especially beneficial for resource-constrained +environments and projects with limited labeled data. MobileNet, known +for its lightweight architecture, is a pre-trained model that has +already learned valuable features from a large dataset (ImageNet). + +![image.png](images_4/media/image9.jpg){width="6.5in" +height="1.9305555555555556in"} + +By leveraging these learned features, you can train a new model for your +specific task with fewer data and computational resources yet achieve +competitive accuracy. + +![image.png](images_4/media/image32.jpg){width="6.5in" +height="2.3055555555555554in"} + +This approach significantly reduces training time and computational +cost, making it ideal for quick prototyping and deployment on embedded +devices where efficiency is paramount. + +Go to the Impulse Design Tab and create the *impulse*, defining an image +size of 96x96 and squashing them (squared form, without crop). Select +Image and Transfer Learning blocks. Save the Impulse. + +![image.png](images_4/media/image16.png){width="6.5in" +height="4.263888888888889in"} + +### **Image Pre-Processing** + +All input QVGA/RGB565 images will be converted to 27,640 features +(96x96x3). + +![image.png](images_4/media/image17.png){width="6.5in" +height="4.319444444444445in"} + +Press \[Save parameters\] and Generate all features: + +![image.png](images_4/media/image5.png){width="6.5in" +height="4.263888888888889in"} + +### **Model Design** + +In 2007, Google introduced +[[MobileNetV1]{.underline}](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html), +a family of general-purpose computer vision neural networks designed +with mobile devices in mind to support classification, detection, and +more. MobileNets are small, low-latency, low-power models parameterized +to meet the resource constraints of various use cases. in 2018, Google +launched [MobileNetV2: Inverted Residuals and Linear +Bottlenecks](https://arxiv.org/abs/1801.04381). + +MobileNet V1 and MobileNet V2 aim for mobile efficiency and embedded +vision applications but differ in architectural complexity and +performance. While both use depthwise separable convolutions to reduce +the computational cost, MobileNet V2 introduces Inverted Residual Blocks +and Linear Bottlenecks to enhance performance. These new features allow +V2 to capture more complex features using fewer parameters, making it +computationally more efficient and generally more accurate than its +predecessor. Additionally, V2 employs a non-linear activation in the +intermediate expansion layer. Still, it uses a linear activation for the +bottleneck layer, a design choice found to preserve important +information through the network better. MobileNet V2 offers a more +optimized architecture for higher accuracy and efficiency and will be +used in this project. + +Although the base MobileNet architecture is already tiny and has low +latency, many times, a specific use case or application may require the +model to be smaller and faster. MobileNets introduces a straightforward +parameter α (alpha) called width multiplier to construct these smaller, +less computationally expensive models. The role of the width multiplier +α is to thin a network uniformly at each layer. + +Edge Impulse Studio has available MobileNetV1 (96x96 images) and V2 +(96x96 and 160x160 images), with several different **α** values (from +0.05 to 1.0). For example, you will get the highest accuracy with V2, +160x160 images, and α=1.0. Of course, there is a trade-off. The higher +the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be +needed to run the model, implying more latency. The smaller footprint +will be obtained at another extreme with MobileNetV1 and α=0.10 (around +53.2K RAM and 101K ROM). + +![image.png](images_4/media/image27.jpg){width="6.5in" +height="3.5277777777777777in"} + +For this project, we will use **MobileNetV2 96x96 0.1**, which estimates +a memory cost of 265.3 KB in RAM. This model should be OK for the Nicla +Vision with 1MB of SRAM. On the Transfer Learning Tab, select this +model: + +![image.png](images_4/media/image24.png){width="6.5in" +height="4.263888888888889in"} + +Another necessary technique to be used with Deep Learning is **Data +Augmentation**. Data augmentation is a method that can help improve the +accuracy of machine learning models, creating additional artificial +data. A data augmentation system makes small, random changes to your +training data during the training process (such as flipping, cropping, +or rotating the images). + +Under the rood, here you can see how Edge Impulse implements a data +Augmentation policy on your data: + + ----------------------------------------------------------------------- + \# Implements the data augmentation policy\ + def augment_image(image, label):\ + \# Flips the image randomly\ + image = tf.image.random_flip_left_right(image)\ + \ + \# Increase the image size, then randomly crop it down to\ + \# the original dimensions\ + resize_factor = random.uniform(1, 1.2)\ + new_height = math.floor(resize_factor \* INPUT_SHAPE\[0\])\ + new_width = math.floor(resize_factor \* INPUT_SHAPE\[1\])\ + image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)\ + image = tf.image.random_crop(image, size=INPUT_SHAPE)\ + \ + \# Vary the brightness of the image\ + image = tf.image.random_brightness(image, max_delta=0.2)\ + \ + return image, label + ----------------------------------------------------------------------- + + ----------------------------------------------------------------------- + +Exposure to these variations during training can help prevent your model +from taking shortcuts by \"memorizing\" superficial clues in your +training data, meaning it may better reflect the deep underlying +patterns in your dataset. + +The final layer of our model will have 12 neurons with a 15% dropout for +overfitting prevention. Here is the Training result: + +![image.png](images_4/media/image31.jpg){width="6.5in" +height="3.5in"} + +The result is excellent, with 77ms of latency, which should result in +13fps (frames per second) during inference. + +### **Model Testing** + +![image.png](images_4/media/image10.jpg){width="6.5in" +height="3.8472222222222223in"} + +Now, you should take the data put apart at the start of the project and +run the trained model having them as input: + +![image.png](images_4/media/image34.png){width="3.1041666666666665in" +height="1.7083333333333333in"} + +The result was, again, excellent. + +![image.png](images_4/media/image12.png){width="6.5in" +height="4.263888888888889in"} + +### **Deploying the model** + +At this point, we can deploy the trained model as.tflite and use the +OpenMV IDE to run it using MicroPython, or we can deploy it as a C/C++ +or an Arduino library. + +![image.png](images_4/media/image28.jpg){width="6.5in" +height="3.763888888888889in"} + +**Arduino Library** + +First, Let\'s deploy it as an Arduino Library: + +![image.png](images_4/media/image48.png){width="6.5in" +height="4.263888888888889in"} + +You should install the library as.zip on the Arduino IDE and run the +sketch nicla_vision_camera.ino available in Examples under your library +name. + +> *Note that Arduino Nicla Vision has, by default, 512KB of RAM +> allocated for the M7 core and an additional 244KB on the M4 address +> space. In the code, this allocation was changed to 288 kB to guarantee +> that the model will run on the device +> (malloc_addblock((void\*)0x30000000, 288 \* 1024);).* + +The result was good, with 86ms of measured latency. + +![image.png](images_4/media/image25.jpg){width="6.5in" +height="3.4444444444444446in"} + +Here is a short video showing the inference results: +[[https://youtu.be/bZPZZJblU-o]{.underline}](https://youtu.be/bZPZZJblU-o) + +**OpenMV** + +It is possible to deploy the trained model to be used with OpenMV in two +ways: as a library and as a firmware. + +Three files are generated as a library: the.tflite model, a list with +the labels, and a simple MicroPython script that can make inferences +using the model. + +![image.png](images_4/media/image26.png){width="6.5in" +height="1.0in"} + +Running this model as a.tflite directly in the Nicla was impossible. So, +we can sacrifice the accuracy using a smaller model or deploy the model +as an OpenMV Firmware (FW). As an FW, the Edge Impulse Studio generates +optimized models, libraries, and frameworks needed to make the +inference. Let\'s explore this last one. + +Select OpenMV Firmware on the Deploy Tab and press \[Build\]. + +![image.png](images_4/media/image3.png){width="6.5in" +height="4.263888888888889in"} + +On your computer, you will find a ZIP file. Open it: + +![Pasted Graphic +64.png](images_4/media/image33.png){width="6.5in" +height="2.625in"} + +Use the Bootloader tool on the OpenMV IDE to load the FW on your board: + +![Pasted Graphic +63.png](images_4/media/image35.jpg){width="6.5in" +height="3.625in"} + +Select the appropriate file (.bin for Nicla-Vision): + +![Pasted Graphic +65.png](images_4/media/image8.png){width="6.5in" +height="1.9722222222222223in"} + +After the download is finished, press OK: + +![DFU firmware update +complete!.png](images_4/media/image40.png){width="3.875in" +height="5.708333333333333in"} + +If a message says that the FW is outdated, DO NOT UPGRADE. Select +\[NO\]. + +![image.png](images_4/media/image42.png){width="4.572916666666667in" +height="2.875in"} + +Now, open the script **ei_image_classification.py** that was downloaded +from the Studio and the.bin file for the Nicla. + +![image.png](images_4/media/image14.png){width="6.5in" +height="4.0in"} + +And run it. Pointing the camera to the objects we want to classify, the +inference result will be displayed on the Serial Terminal. + +![image.png](images_4/media/image37.png){width="6.5in" +height="3.736111111111111in"} + +**Changing Code to add labels:** + +The code provided by Edge Impulse can be modified so that we can see, +for test reasons, the inference result directly on the image displayed +on the OpenMV IDE. + +[[Upload the code from +GitHub,]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification.py) +or modify it as below: + + ----------------------------------------------------------------------- + \# Marcelo Rovai - NICLA Vision - Image Classification\ + \# Adapted from Edge Impulse - OpenMV Image Classification Example\ + \# \@24Aug23\ + \ + import sensor, image, time, os, tf, uos, gc\ + \ + sensor.reset() \# Reset and initialize the sensor.\ + sensor.set_pixformat(sensor.RGB565) \# Set pxl fmt to RGB565 (or + GRAYSCALE)\ + sensor.set_framesize(sensor.QVGA) \# Set frame size to QVGA (320x240)\ + sensor.set_windowing((240, 240)) \# Set 240x240 window.\ + sensor.skip_frames(time=2000) \# Let the camera adjust.\ + \ + net = None\ + labels = None\ + \ + try:\ + \# Load built in model\ + labels, net = tf.load_builtin_model(\'trained\')\ + except Exception as e:\ + raise Exception(e)\ + \ + clock = time.clock()\ + while(True):\ + clock.tick() \# Starts tracking elapsed time.\ + \ + img = sensor.snapshot()\ + \ + \# default settings just do one detection\ + for obj in net.classify(img,\ + min_scale=1.0,\ + scale_mul=0.8,\ + x_overlap=0.5,\ + y_overlap=0.5):\ + fps = clock.fps()\ + lat = clock.avg()\ + \ + print(\"\*\*\*\*\*\*\*\*\*\*\\nPrediction:\")\ + img.draw_rectangle(obj.rect())\ + \# This combines the labels and confidence values into a list of + tuples\ + predictions_list = list(zip(labels, obj.output()))\ + \ + max_val = predictions_list\[0\]\[1\]\ + max_lbl = \'background\'\ + for i in range(len(predictions_list)):\ + val = predictions_list\[i\]\[1\]\ + lbl = predictions_list\[i\]\[0\]\ + \ + if val \> max_val:\ + max_val = val\ + max_lbl = lbl\ + \ + \# Print label with the highest probability\ + if max_val \< 0.5:\ + max_lbl = \'uncertain\'\ + print(\"{} with a prob of {:.2f}\".format(max_lbl, max_val))\ + print(\"FPS: {:.2f} fps ==\> latency: {:.0f} ms\".format(fps, lat))\ + \ + \# Draw label with highest probability to image viewer\ + img.draw_string(\ + 10, 10,\ + max_lbl + \"\\n{:.2f}\".format(max_val),\ + mono_space = False,\ + scale=2\ + ) + ----------------------------------------------------------------------- + + ----------------------------------------------------------------------- + +Here you can see the result: + +![image.png](images_4/media/image47.jpg){width="6.5in" +height="2.9444444444444446in"} + +Note that the latency (136 ms) is almost double what we got directly +with the Arduino IDE. This is because we are using the IDE as an +interface and the time to wait for the camera to be ready. If we start +the clock just before the inference: + +![image.png](images_4/media/image13.jpg){width="6.5in" +height="2.0972222222222223in"} + +The latency will drop to only 71 ms. + +![image.png](images_4/media/image1.jpg){width="3.5520833333333335in" +height="1.53125in"} + +### ***OpenMV Cam runs about half as fast when connected to the IDE. The FPS should increase once disconnected.*** + +### **Post-Processing with LEDs** + +When working with embedded machine learning, we are looking for devices +that can continually proceed with the inference and result, taking some +action directly on the physical world and not displaying the result on a +connected computer. To simulate this, we will define one LED to light up +for each one of the possible inference results. + +![image.png](images_4/media/image38.jpg){width="6.5in" +height="3.236111111111111in"} + +For that, we should [[upload the code from +GitHub]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification_LED.py) +or change the last code to include the LEDs: + + ----------------------------------------------------------------------- + \# Marcelo Rovai - NICLA Vision - Image Classification with LEDs\ + \# Adapted from Edge Impulse - OpenMV Image Classification Example\ + \# \@24Aug23\ + \ + import sensor, image, time, os, tf, uos, gc, pyb\ + \ + ledRed = pyb.LED(1)\ + ledGre = pyb.LED(2)\ + ledBlu = pyb.LED(3)\ + \ + sensor.reset() \# Reset and initialize the sensor.\ + sensor.set_pixformat(sensor.RGB565) \# Set pixl fmt to RGB565 (or + GRAYSCALE)\ + sensor.set_framesize(sensor.QVGA) \# Set frame size to QVGA (320x240)\ + sensor.set_windowing((240, 240)) \# Set 240x240 window.\ + sensor.skip_frames(time=2000) \# Let the camera adjust.\ + \ + net = None\ + labels = None\ + \ + ledRed.off()\ + ledGre.off()\ + ledBlu.off()\ + \ + try:\ + \# Load built in model\ + labels, net = tf.load_builtin_model(\'trained\')\ + except Exception as e:\ + raise Exception(e)\ + \ + clock = time.clock()\ + \ + \ + def setLEDs(max_lbl):\ + \ + if max_lbl == \'uncertain\':\ + ledRed.on()\ + ledGre.off()\ + ledBlu.off()\ + \ + if max_lbl == \'periquito\':\ + ledRed.off()\ + ledGre.on()\ + ledBlu.off()\ + \ + if max_lbl == \'robot\':\ + ledRed.off()\ + ledGre.off()\ + ledBlu.on()\ + \ + if max_lbl == \'background\':\ + ledRed.off()\ + ledGre.off()\ + ledBlu.off()\ + \ + \ + while(True):\ + img = sensor.snapshot()\ + clock.tick() \# Starts tracking elapsed time.\ + \ + \# default settings just do one detection.\ + for obj in net.classify(img,\ + min_scale=1.0,\ + scale_mul=0.8,\ + x_overlap=0.5,\ + y_overlap=0.5):\ + fps = clock.fps()\ + lat = clock.avg()\ + \ + print(\"\*\*\*\*\*\*\*\*\*\*\\nPrediction:\")\ + img.draw_rectangle(obj.rect())\ + \# This combines the labels and confidence values into a list of + tuples\ + predictions_list = list(zip(labels, obj.output()))\ + \ + max_val = predictions_list\[0\]\[1\]\ + max_lbl = \'background\'\ + for i in range(len(predictions_list)):\ + val = predictions_list\[i\]\[1\]\ + lbl = predictions_list\[i\]\[0\]\ + \ + if val \> max_val:\ + max_val = val\ + max_lbl = lbl\ + \ + \# Print label and turn on LED with the highest probability\ + if max_val \< 0.8:\ + max_lbl = \'uncertain\'\ + \ + setLEDs(max_lbl)\ + \ + print(\"{} with a prob of {:.2f}\".format(max_lbl, max_val))\ + print(\"FPS: {:.2f} fps ==\> latency: {:.0f} ms\".format(fps, lat))\ + \ + \# Draw label with highest probability to image viewer\ + img.draw_string(\ + 10, 10,\ + max_lbl + \"\\n{:.2f}\".format(max_val),\ + mono_space = False,\ + scale=2\ + ) + ----------------------------------------------------------------------- + + ----------------------------------------------------------------------- + +Now, each time that a class gets a result superior of 0.8, the +correspondent LED will be light on as below: + +- Led Red 0n: Uncertain (no one class is over 0.8) + +- Led Green 0n: Periquito \> 0.8 + +- Led Blue 0n: Robot \> 0.8 + +- All LEDs Off: Background \> 0.8 + +Here is the result: + +![image.png](images_4/media/image18.jpg){width="6.5in" +height="3.6527777777777777in"} + +In more detail + +![image.png](images_4/media/image21.jpg){width="6.5in" +height="2.0972222222222223in"} + +### **Image Classification (non-official) Benchmark** + +Several development boards can be used for embedded machine learning +(tinyML), and the most common ones for Computer Vision applications +(with low energy), are the ESP32 CAM, the Seeed XIAO ESP32S3 Sense, the +Arduinos Nicla Vison, and Portenta. + +![image.png](images_4/media/image19.jpg){width="6.5in" +height="4.194444444444445in"} + +Using the opportunity, the same trained model was deployed on the +ESP-CAM, the XIAO, and Portenta (in this one, the model was trained +again, using grayscaled images to be compatible with its camera. Here is +the result, deploying the models as Arduino\'s Library: + +![image.png](images_4/media/image4.jpg){width="6.5in" +height="3.4444444444444446in"} + +### **Conclusion** + +Before we finish, consider that Computer Vision is more than just image +classification. For example, you can develop Edge Machine Learning +projects around vision in several areas, such as: + +- **Autonomous Vehicles**: Use sensor fusion, lidar data, and computer + > vision algorithms to navigate and make decisions. + +- **Healthcare**: Automated diagnosis of diseases through MRI, X-ray, + > and CT scan image analysis + +- **Retail**: Automated checkout systems that identify products as + > they pass through a scanner. + +- **Security and Surveillance**: Facial recognition, anomaly + > detection, and object tracking in real-time video feeds. + +- **Augmented Reality**: Object detection and classification to + > overlay digital information in the real world. + +- **Industrial Automation**: Visual inspection of products, predictive + > maintenance, and robot and drone guidance. + +- **Agriculture**: Drone-based crop monitoring and automated + > harvesting. + +- **Natural Language Processing**: Image captioning and visual + > question answering. + +- **Gesture Recognition**: 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