diff --git a/.DS_Store b/.DS_Store
index 8803425..0c17f09 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/chapter_4-4.qmd b/chapter_4-4.qmd
index 1a94553..9597325 100644
--- a/chapter_4-4.qmd
+++ b/chapter_4-4.qmd
@@ -49,7 +49,7 @@ Below is the general board pinout:
-> For more details, please refer to Seeed Studio WiKi page:
+> For more details, please refer to the Seeed Studio WiKi page:
> https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/
## 4.4.3 Installing the XIAO ESP32S3 Sense on Arduino IDE
@@ -66,6 +66,10 @@ Next, open boards manager. Go to **Tools** > **Board** > **Boards Manager...** a
+> ⚠️ **Attention**
+>
+> Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.
+
On **Tools**, select the Board (**XIAO ESP32S3**):
@@ -225,7 +229,31 @@ The program will do the following tasks:
Inspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial: [ESP32-CAM Take Photo and Display in Web Server](https://randomnerdtutorials.com/esp32-cam-take-photo-display-web-server/), which I invite all of you to visit.
-## 4.4.9 Fruits versus Veggies - A TinyML Image Classification project
+**Using the CameraWebServer**
+
+In `File \> Examples \> ESP32 \> Camera`, select `CameraWebServer`
+
+You also should comment on all cameras' models, except the XIAO model pins:
+
+`#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM`
+
+and do not forget the `Tools` to enable the PSRAM.
+
+Enter your wifi credentials and upload the code to the device:
+
+
+
+If the code is executed correctly, you should see the address on the Serial Monitor:
+
+
+
+Copy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select `[START STREAM]`. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the \[Save\] button.
+
+
+
+That's it! You can save the images directly in your computer to be used on projects.
+
+## 4.4.9 Fruits versus Veggies - A TinyML Image Classification Project
@@ -235,11 +263,11 @@ Now that we have an embedded camera running, it is time to try image classificat
-The whole idea of our project will be training a model and proceeding with inference on the XIAO ESP32S3 Sense. For training, we should find some data **(in fact, tons of data!**).
+The whole idea of our project will be to train a model and proceed with inference on the XIAO ESP32S3 Sense. For training, we should find some data **(in fact, tons of data!**).
*But first of all, we need a goal! What do we want to classify?*
-With TinyML, a set of technics associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory in this situation). We will differentiate **apples** from **bananas** and **potatoes** (you can try other categories)**.**
+With TinyML, a set of techniques associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory). We will differentiate **apples** from **bananas** and **potatoes** (you can try other categories)**.**
So, let's find a specific dataset that includes images from those categories. Kaggle is a good start:
@@ -254,11 +282,11 @@ Each category is split into the **train** (100 images), **test** (10 images), an
- Download the dataset from the Kaggle website to your computer.
-> Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the sketch discussed in the last section.
+> Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the codes discussed in the last section.
## 4.4.10 Training the model with Edge Impulse Studio
-We will use the Edge Impulse Studio for training our model. [Edge Impulse](https://www.edgeimpulse.com/) is a leading development platform for machine learning on edge devices.
+We will use the Edge Impulse Studio to train our model. As you know, [Edge Impulse](https://www.edgeimpulse.com/) is a leading development platform for machine learning on edge devices.
Enter your account credentials (or create a free account) at Edge Impulse. Next, create a new project:
@@ -270,47 +298,47 @@ Next, on the UPLOAD DATA section, upload from your computer the files from chose
-You should now have your training dataset, split in three classes of data:
+You should now have your training dataset split in three classes of data:
-> You can upload extra data for further model testing or split the training data. I will leave as it, to use most data possible.
+> You can upload extra data for further model testing or split the training data. I will leave it as it is to use the most data possible.
**Impulse Design**
-An impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data.
+> An impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data.
-As mentioned, classifying images is the most common use of Deep Learning, but much data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousand of images to "teach or model" to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We called this technic "Transfer Learning" (TL).
+Classifying images is the most common use of deep learning, but a lot of data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousands of images to "teach or model" to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We call this technique "Transfer Learning" (TL).
With TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case).
-So, starting from the raw images, we will resize them (96x96) pixels and so, feeding them to our Transfer Learning block:
+So, starting from the raw images, we will resize them (96x96) pixels and feed them to our Transfer Learning block:
**Pre-processing (Feature generation)**
-Besides resizing the images, we should change them to Grayscale instead to keep the actual RGB color depth. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.
+Besides resizing the images, we can change them to Grayscale or keep the actual RGB color depth. Let's start selecting `Grayscale`. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.
-Do not forget to "Save parameters." This will generate the features to be used in training.
+Do not forget to `[Save parameters]`." This will generate the features to be used in training.
**Training (Transfer Learning & Data Augmentation)**
-In 2007, Google introduced [MobileNetV1,](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 2007, Google introduced [MobileNetV1,](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.
-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. MobileNet introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller and less computationally expensive models. The role of the width multiplier α is to thin a network uniformly at each layer.
+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. MobileNet 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 MobileNet V1 (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 highest the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be needed to run the model and imply more latency.
+Edge Impulse Studio has available MobileNet V1 (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 **MobileNet V1** and α=0.10 (around 53.2K RAM and 101K ROM).
When we first published this project to be running on an ESP32-CAM, we stayed at the lower side of possibilities which guaranteed the inference with small latency but not with high accuracy. For this first pass, we will keep this model design (**MobileNet V1** and α=0.10).
-Another important technic 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).
+Another necessary technique to use 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:
@@ -344,7 +372,7 @@ The result is not great. The model reached around 77% of accuracy, but the amoun
**Deployment**
-The trained model will be deployed as a.zip Arduino library:
+The trained model will be deployed as a .zip Arduino library:
@@ -366,7 +394,7 @@ You can see that the first line of code is exactly the calling of a library with
#include
```
-Of course, this is a generic code (a "template"), that only gets one sample of raw data (stored on the variable: *features = {}* and run the classifier, doing the inference. The result is shown on Serial Monitor.
+Of course, this is a generic code (a "template") that only gets one sample of raw data (stored on the variable: features = {} and runs the classifier, doing the inference. The result is shown on the Serial Monitor.
We should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes.
@@ -380,15 +408,15 @@ Returning to your project (Tab Image), copy one of the Raw Data Sample:
-Edge Impulse included the [library ESP NN](https://github.com/espressif/esp-nn) in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (runing at Arduino IDE).
+Edge Impulse included the [library ESP NN](https://github.com/espressif/esp-nn) in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (running at Arduino IDE).
-Now, when running the inference, you should get, as a result, the highest score for "banana".
+Now, when running the inference, you should get the highest score for "banana."
Great news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. It is a better result than the ESP32 CAM (525ms of latency).
-Now, we should incorporate the camera and classify images in real-time.
+Now, we should incorporate the camera and classify images in real time.
Go to the Arduino IDE Examples and download from your project the sketch esp32_camera:
@@ -421,7 +449,7 @@ Here you can see the resulting code:
The modified sketch can be downloaded from GitHub: [xiao_esp32s3_camera](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_camera).
-> Note that you can optionally keep the pins as a.h file as we did on previous sections.
+> Note that you can optionally keep the pins as a .h file as we did in previous sections.
Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor.
@@ -443,9 +471,11 @@ Now, let's go to the other side of the model size. Let's select a MobilinetV2 96
-Even with a bigger model, the accuracy is not good, and worst, the amount of memory necessary to run the model increases five times, with latency increasing seven times (note that the performance here is estimated with a smaller device, the ESP-EYE. So, the real inference with the ESP32S3 should be better).
+Even with a bigger model, the accuracy is not that good, and the amount of memory necessary to run the model increases five times, with latency increasing seven times
+
+> Note that the performance here is estimated with a smaller device, the ESP-EYE. The actual inference with the ESP32S3 should be better.
-> To make our model better, we will probably need more images to be trained.
+To improve our model, we will need to train more images.
Even though our model did not improve in terms of accuracy, let's test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch.
@@ -457,7 +487,7 @@ Doing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the
-In our tests, this option works with MobileNet V2 but not V1. So, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the resultin accuraccy was higher.
+For test, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the result in accuraccy was higher.
@@ -467,7 +497,67 @@ Deploying the model, I got an inference of only 135ms, remembering that the XIAO
-## 4.4.13 Conclusion
+## 4.4.13 Runing inference on the SenseCraft-Web-Toolkit
+
+On big limitation of viewing inference on Arduino IDE, is that we can not see what the camera is really focusing. A good alternative for that is the **SenseCraft-Web-Toolkit**, a visual model deployment tool provided by [SSCMA](https://sensecraftma.seeed.cc/) (Seeed SenseCraft Model Assistant). With this tool, you can easily deploy models to various platforms through simple operations. The tool provides a user-friendly interface and does not require any coding.
+
+Follow the following steps to start the SenseCraft-Web-Toolkit:
+
+1. Open the SenseCraft-Web-Toolkit [website](https://seeed-studio.github.io/SenseCraft-Web-Toolk).
+2. Connect the XIAO to your computer:
+
+- Having the XIAO connected, select it as below:
+
+
+
+- Select the device/Port and press `[Connect]`:
+
+
+
+> You can try several Computer Vision models previously uploaded by Seeed Studio. Try them and have fun!
+
+In our case, we will use the blue button at the botton of the page: `[Upload Custom AI Model]`.
+
+But first, we will need to download from Edge Impulse Studio, our **quantized .tflite** model.
+
+3. Go to your project at Edge Impulse Studio, or clone this one:
+
+- [XIAO-ESP32S3-CAM-Fruits-vs-Veggies-v1-ESP-NN](https://studio.edgeimpulse.com/public/228516/live)
+
+4. On `Dashboard`, download the model ("block output"): `Transfer learning mdodel - TensorFlow Lite (int8 quantized)`
+
+
+
+5. On SenseCraft-Web-Toolkit, use the blue button at the botton of the page: `[Upload Custom AI Model]`. A window will pop-up. Enter with the Model file that you downloaded to your computer from Edge Impulse Studio, choos a Model Name and enter with labels (ID:Object):
+
+
+
+> Note that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: apple, banana, potato).
+
+After a few seconds (or minutes), the model will be uploaded to your device and the camera image will appear in real-time on the Preview Sector:
+
+
+
+The Classification result will be at at the top of the image. You can also select the Confidence of your inference cursor `Confidence`.
+
+Clicking in the top button (Device Log), you can open a Serial Monitor to follow the inference, same that we have done with the Arduino IDE:
+
+
+
+On Device Log, you will get Information as:
+
+
+
+- Preprocess time (image capture and Crop): 4ms;
+- Inference time (model latency): 106ms,
+- Postprocess time (display of the image and inclusion of data): 0ms.
+- Output tensor (classes), for example: [[89,0]]; where 0 is Apple (and 1is banana and 2 is potato)
+
+Here are other screen shots:
+
+
+
+## 4.4.14 Conclusion
The XIAO ESP32S3 Sense is a very flexible, not expensive, and easy-to-program device. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication.
diff --git a/chapter_4-5.qmd b/chapter_4-5.qmd
index fa29e3f..0a7e33c 100644
--- a/chapter_4-5.qmd
+++ b/chapter_4-5.qmd
@@ -77,9 +77,13 @@ You can use the XIAO, your phone, or other devices for the image capture. Here,
### 4.5.4.1 Collecting Dataset with the XIAO ESP32S3
-Open the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On `File \> Examples \> ESP32 \> Camera`, select `CameraWebServer`. On the BOARDS MANAGER panel, confirm that you have installed the latest "stable" package.
+Open the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On `File \> Examples \> ESP32 \> Camera`, select `CameraWebServer`.
-> The current alpha versions (3.0) did not work correctly with the XIAO. The 2.0.14 works fine.
+On the BOARDS MANAGER panel, confirm that you have installed the latest "stable" package.
+
+> ⚠️ **Attention**
+>
+> Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.
You also should comment on all cameras' models, except the XIAO model pins:
diff --git a/docs/chapter_4-4.html b/docs/chapter_4-4.html
index f23d1cd..4ad7ee5 100644
--- a/docs/chapter_4-4.html
+++ b/docs/chapter_4-4.html
@@ -386,11 +386,12 @@
For more details, please refer to Seeed Studio WiKi page: https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/
+
For more details, please refer to the Seeed Studio WiKi page: https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/
@@ -463,6 +464,10 @@
Next, open boards manager. Go to Tools > Board > Boards Manager… and enter with esp32. Select and install the most updated and stable package (avoid alpha versions) :
+
+
⚠️ Attention
+
Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.
+
On Tools, select the Board (XIAO ESP32S3):
Last but not least, select the Port where the ESP32S3 is connected.
@@ -582,16 +587,33 @@
4.4.8 Testing WiFi
This program can be used for an image dataset capture with an Image Classification project.
Inspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial: ESP32-CAM Take Photo and Display in Web Server, which I invite all of you to visit.
+
Using the CameraWebServer
+
In File \> Examples \> ESP32 \> Camera, select CameraWebServer
+
You also should comment on all cameras’ models, except the XIAO model pins:
+
#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM
+
and do not forget the Tools to enable the PSRAM.
+
Enter your wifi credentials and upload the code to the device:
+
+
If the code is executed correctly, you should see the address on the Serial Monitor:
+
+
+
+image-20240214163034559
+
+
+
Copy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select [START STREAM]. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the [Save] button.
+
+
That’s it! You can save the images directly in your computer to be used on projects.
-
4.4.9 Fruits versus Veggies - A TinyML Image Classification project
+
4.4.9 Fruits versus Veggies - A TinyML Image Classification Project
Now that we have an embedded camera running, it is time to try image classification. For comparative motive, we will replicate the same image classification project developed to be used with an old ESP2-CAM:
The whole idea of our project will be training a model and proceeding with inference on the XIAO ESP32S3 Sense. For training, we should find some data (in fact, tons of data!).
+
The whole idea of our project will be to train a model and proceed with inference on the XIAO ESP32S3 Sense. For training, we should find some data (in fact, tons of data!).
But first of all, we need a goal! What do we want to classify?
-
With TinyML, a set of technics associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory in this situation). We will differentiate apples from bananas and potatoes (you can try other categories).
+
With TinyML, a set of techniques associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory). We will differentiate apples from bananas and potatoes (you can try other categories).
So, let’s find a specific dataset that includes images from those categories. Kaggle is a good start:
This dataset contains images of the following food items:
@@ -604,40 +626,42 @@
-
Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the sketch discussed in the last section.
+
Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the codes discussed in the last section.
4.4.10 Training the model with Edge Impulse Studio
-
We will use the Edge Impulse Studio for training our model. Edge Impulse is a leading development platform for machine learning on edge devices.
+
We will use the Edge Impulse Studio to train our model. As you know, Edge Impulse is a leading development platform for machine learning on edge devices.
Enter your account credentials (or create a free account) at Edge Impulse. Next, create a new project:
Data Acquisition
Next, on the UPLOAD DATA section, upload from your computer the files from chosen categories:
-
You should now have your training dataset, split in three classes of data:
+
You should now have your training dataset split in three classes of data:
-
You can upload extra data for further model testing or split the training data. I will leave as it, to use most data possible.
+
You can upload extra data for further model testing or split the training data. I will leave it as it is to use the most data possible.
Impulse Design
+
An impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data.
-
As mentioned, classifying images is the most common use of Deep Learning, but much data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousand of images to “teach or model” to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We called this technic “Transfer Learning” (TL).
+
+
Classifying images is the most common use of deep learning, but a lot of data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousands of images to “teach or model” to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We call this technique “Transfer Learning” (TL).
With TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case).
-
So, starting from the raw images, we will resize them (96x96) pixels and so, feeding them to our Transfer Learning block:
+
So, starting from the raw images, we will resize them (96x96) pixels and feed them to our Transfer Learning block:
Pre-processing (Feature generation)
-
Besides resizing the images, we should change them to Grayscale instead to keep the actual RGB color depth. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.
+
Besides resizing the images, we can change them to Grayscale or keep the actual RGB color depth. Let’s start selecting Grayscale. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.
-
Do not forget to “Save parameters.” This will generate the features to be used in training.
+
Do not forget to [Save parameters].” This will generate the features to be used in training.
Training (Transfer Learning & Data Augmentation)
-
In 2007, Google introduced MobileNetV1,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.
-
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. MobileNet introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller and 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 MobileNet V1 (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 highest the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be needed to run the model and imply more latency.
+
In 2007, Google introduced MobileNetV1, 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.
+
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. MobileNet 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 MobileNet V1 (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 MobileNet V1 and α=0.10 (around 53.2K RAM and 101K ROM).
When we first published this project to be running on an ESP32-CAM, we stayed at the lower side of possibilities which guaranteed the inference with small latency but not with high accuracy. For this first pass, we will keep this model design (MobileNet V1 and α=0.10).
-
Another important technic 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).
+
Another necessary technique to use 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 policydef augment_image(image, label):
@@ -661,7 +685,7 @@
The result is not great. The model reached around 77% of accuracy, but the amount of RAM expected to be used during the inference is relatively small (around 60 KBytes), which is very good.
Deployment
-
The trained model will be deployed as a.zip Arduino library:
+
The trained model will be deployed as a .zip Arduino library:
Open your Arduino IDE, and under Sketch, go to Include Library and add.ZIP Library. Select the file you download from Edge Impulse Studio, and that’s it!
@@ -671,18 +695,18 @@
You can see that the first line of code is exactly the calling of a library with all the necessary stuff for running inference on your device.
Of course, this is a generic code (a “template”), that only gets one sample of raw data (stored on the variable: features = {} and run the classifier, doing the inference. The result is shown on Serial Monitor.
+
Of course, this is a generic code (a “template”) that only gets one sample of raw data (stored on the variable: features = {} and runs the classifier, doing the inference. The result is shown on the Serial Monitor.
We should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes.
Returning to your project (Tab Image), copy one of the Raw Data Sample:
9, 216 features will be copied to the clipboard. This is the input tensor (a flattened image of 96x96x1), in this case, bananas. Past this Input tensor on features[] = {0xb2d77b, 0xb5d687, 0xd8e8c0, 0xeaecba, 0xc2cf67, …}
-
Edge Impulse included the library ESP NN in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (runing at Arduino IDE).
-
Now, when running the inference, you should get, as a result, the highest score for “banana”.
+
Edge Impulse included the library ESP NN in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (running at Arduino IDE).
+
Now, when running the inference, you should get the highest score for “banana.”
Great news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. It is a better result than the ESP32 CAM (525ms of latency).
-
Now, we should incorporate the camera and classify images in real-time.
+
Now, we should incorporate the camera and classify images in real time.
Go to the Arduino IDE Examples and download from your project the sketch esp32_camera:
You should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75:
Note that you can optionally keep the pins as a.h file as we did on previous sections.
+
Note that you can optionally keep the pins as a .h file as we did in previous sections.
Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor.
@@ -722,16 +746,17 @@
4.4.11 Testing
4.4.12 Testing with a bigger model
Now, let’s go to the other side of the model size. Let’s select a MobilinetV2 96x96 0.35, having as input RGB images.
-
Even with a bigger model, the accuracy is not good, and worst, the amount of memory necessary to run the model increases five times, with latency increasing seven times (note that the performance here is estimated with a smaller device, the ESP-EYE. So, the real inference with the ESP32S3 should be better).
+
Even with a bigger model, the accuracy is not that good, and the amount of memory necessary to run the model increases five times, with latency increasing seven times
-
To make our model better, we will probably need more images to be trained.
+
Note that the performance here is estimated with a smaller device, the ESP-EYE. The actual inference with the ESP32S3 should be better.
+
To improve our model, we will need to train more images.
Even though our model did not improve in terms of accuracy, let’s test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch.
Let’s redeploy the model. If the EON Compiler is enabled when you generate the library, the total memory needed for inference should be reduced, but it has no influence on accuracy.
Doing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the latency was of 219ms, what it is great for such bigger model.
-
In our tests, this option works with MobileNet V2 but not V1. So, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the resultin accuraccy was higher.
+
For test, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the result in accuraccy was higher.
Note that the estimated latency for an Arduino Portenta (ou Nicla), running with a clock of 480MHz is 45ms.
@@ -739,8 +764,62 @@
4.4.12 Testing
Deploying the model, I got an inference of only 135ms, remembering that the XIAO run with half of the clock used by the Portenta/Nicla (240MHz):
+
+
4.4.13 Runing inference on the SenseCraft-Web-Toolkit
+
On big limitation of viewing inference on Arduino IDE, is that we can not see what the camera is really focusing. A good alternative for that is the SenseCraft-Web-Toolkit, a visual model deployment tool provided by SSCMA (Seeed SenseCraft Model Assistant). With this tool, you can easily deploy models to various platforms through simple operations. The tool provides a user-friendly interface and does not require any coding.
+
Follow the following steps to start the SenseCraft-Web-Toolkit:
On Dashboard, download the model (“block output”): Transfer learning mdodel - TensorFlow Lite (int8 quantized)
+
+
+
+
On SenseCraft-Web-Toolkit, use the blue button at the botton of the page: [Upload Custom AI Model]. A window will pop-up. Enter with the Model file that you downloaded to your computer from Edge Impulse Studio, choos a Model Name and enter with labels (ID:Object):
+
+
+
+
Note that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: apple, banana, potato).
+
+
After a few seconds (or minutes), the model will be uploaded to your device and the camera image will appear in real-time on the Preview Sector:
+
+
The Classification result will be at at the top of the image. You can also select the Confidence of your inference cursor Confidence.
+
Clicking in the top button (Device Log), you can open a Serial Monitor to follow the inference, same that we have done with the Arduino IDE:
+
+
On Device Log, you will get Information as:
+
+
+
Preprocess time (image capture and Crop): 4ms;
+
Inference time (model latency): 106ms,
+
Postprocess time (display of the image and inclusion of data): 0ms.
+
Output tensor (classes), for example: [[89,0]]; where 0 is Apple (and 1is banana and 2 is potato)
+
+
Here are other screen shots:
+
+
-
4.4.13 Conclusion
+
4.4.14 Conclusion
The XIAO ESP32S3 Sense is a very flexible, not expensive, and easy-to-program device. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication.
On the GitHub repository, you will find the last version of the codes: XIAO-ESP32S3-Sense.
You can use the XIAO, your phone, or other devices for the image capture. Here, we will use the XIAO with a code in the ESP32 library.
4.5.4.1 Collecting Dataset with the XIAO ESP32S3
-
Open the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On File \> Examples \> ESP32 \> Camera, select CameraWebServer. On the BOARDS MANAGER panel, confirm that you have installed the latest “stable” package.
+
Open the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On File \> Examples \> ESP32 \> Camera, select CameraWebServer.
+
On the BOARDS MANAGER panel, confirm that you have installed the latest “stable” package.
-
The current alpha versions (3.0) did not work correctly with the XIAO. The 2.0.14 works fine.
+
⚠️ Attention
+
Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.
You also should comment on all cameras’ models, except the XIAO model pins:
#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM
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index 7422159..01cdf41 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -599,14 +599,14 @@
"href": "chapter_4-4.html#introduction",
"title": "4.4 Image Classification",
"section": "4.4.2 Introduction",
- "text": "4.4.2 Introduction\nMore 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!\n\nIn 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”!\nSeeed Studio released a new affordable development board, the XIAO ESP32S3 Sense, which integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board is a great tool to start with TinyML (intelligent voice and vision AI).\n\nXIAO ESP32S3 Sense Main Features\n\nPowerful MCU Board: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino / MicroPython supported\nAdvanced Functionality: Detachable OV2640 camera sensor for 1600 * 1200 resolution, compatible with OV5640 camera sensor, integrating an additional digital microphone\nElaborate Power Design: Lithium battery charge management capability offer four power consumption model, which allows for deep sleep mode with power consumption as low as 14μA\nGreat Memory for more Possibilities: Offer 8MB PSRAM and 8MB FLASH, supporting SD card slot for external 32GB FAT memory\nOutstanding RF performance: Support 2.4GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna\nThumb-sized Compact Design: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices\n\n\nBelow is the general board pinout:\n\n\nFor more details, please refer to Seeed Studio WiKi page: https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/"
+ "text": "4.4.2 Introduction\nMore 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!\n\nIn 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”!\nSeeed Studio released a new affordable development board, the XIAO ESP32S3 Sense, which integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board is a great tool to start with TinyML (intelligent voice and vision AI).\n\nXIAO ESP32S3 Sense Main Features\n\nPowerful MCU Board: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino / MicroPython supported\nAdvanced Functionality: Detachable OV2640 camera sensor for 1600 * 1200 resolution, compatible with OV5640 camera sensor, integrating an additional digital microphone\nElaborate Power Design: Lithium battery charge management capability offer four power consumption model, which allows for deep sleep mode with power consumption as low as 14μA\nGreat Memory for more Possibilities: Offer 8MB PSRAM and 8MB FLASH, supporting SD card slot for external 32GB FAT memory\nOutstanding RF performance: Support 2.4GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna\nThumb-sized Compact Design: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices\n\n\nBelow is the general board pinout:\n\n\nFor more details, please refer to the Seeed Studio WiKi page: https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/"
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"title": "4.4 Image Classification",
"section": "4.4.3 Installing the XIAO ESP32S3 Sense on Arduino IDE",
- "text": "4.4.3 Installing the XIAO ESP32S3 Sense on Arduino IDE\nOn Arduino IDE, navigate to File > Preferences, and fill in the URL:\nhttps://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json\non the field ==> Additional Boards Manager URLs\n\nNext, open boards manager. Go to Tools > Board > Boards Manager… and enter with esp32. Select and install the most updated and stable package (avoid alpha versions) :\n\nOn Tools, select the Board (XIAO ESP32S3):\n\nLast but not least, select the Port where the ESP32S3 is connected.\nThat is it! The device should be OK. Let’s do some tests."
+ "text": "4.4.3 Installing the XIAO ESP32S3 Sense on Arduino IDE\nOn Arduino IDE, navigate to File > Preferences, and fill in the URL:\nhttps://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json\non the field ==> Additional Boards Manager URLs\n\nNext, open boards manager. Go to Tools > Board > Boards Manager… and enter with esp32. Select and install the most updated and stable package (avoid alpha versions) :\n\n\n⚠️ Attention\nAlpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.\n\nOn Tools, select the Board (XIAO ESP32S3):\n\nLast but not least, select the Port where the ESP32S3 is connected.\nThat is it! The device should be OK. Let’s do some tests."
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"title": "4.4 Image Classification",
"section": "4.4.8 Testing WiFi",
- "text": "4.4.8 Testing WiFi\nOne of the differentiators of the XIAO ESP32S3 is its WiFi capability. So, let’s test its radio, scanning the wifi networks around it. You can do it by running one of the code examples on the board.\nGo to Arduino IDE Examples and look for WiFI ==> WiFIScan\nOn the Serial monitor, you should see the wifi networks (SSIDs and RSSIs) in the range of your device. Here is what I got on the lab:\n\nSimple WiFi Server (Turning LED ON/OFF)\nLet’s test the device’s capability to behave as a WiFi Server. We will host a simple page on the device that sends commands to turn the XIAO built-in LED ON and OFF.\nLike before, go to GitHub to download the folder with the sketch: SimpleWiFiServer.\nBefore running the sketch, you should enter your network credentials:\nconst char* ssid = \"Your credentials here\";\nconst char* password = \"Your credentials here\";\nYou can monitor how your server is working with the Serial Monitor.\n\nTake the IP address and enter it on your browser:\n\nYou will see a page with links that can turn ON and OFF the built-in LED of your XIAO.\nStreaming video to Web\nNow that you know that you can send commands from the webpage to your device, let’s do the reverse. Let’s take the image captured by the camera and stream it to a webpage:\nDownload from GitHub the folder that contains the code: XIAO-ESP32S3-Streeming_Video.ino.\n\nRemember that the folder contains not only the.ino file, but also a couple of.h files, necessary to handle the camera.\n\nEnter your credentials and run the sketch. On the Serial monitor, you can find the page address to enter in your browser:\n\nOpen the page on your browser (wait a few seconds to start the streaming). That’s it.\n\nStreamlining what your camera is “seen” can be important when you position it to capture a dataset for an ML project (for example, using the code “take_phots_commands.ino”.\nOf course, we can do both things simultaneously, show what the camera is seeing on the page, and send a command to capture and save the image on the SD card. For that, you can use the code Camera_HTTP_Server_STA which folder can be downloaded from GitHub.\n\nThe program will do the following tasks:\n\nSet the camera to JPEG output mode.\nCreate a web page (for example ==> http://192.168.4.119//). The correct address will be displayed on the Serial Monitor.\nIf server.on (“/capture”, HTTP_GET, serverCapture), the program takes a photo and sends it to the Web.\nIt is possible to rotate the image on webPage using the button [ROTATE]\nThe command [CAPTURE] only will preview the image on the webpage, showing its size on Serial Monitor\nThe [SAVE] command will save an image on the SD Card, also showing the image on the web.\nSaved images will follow a sequential naming (image1.jpg, image2.jpg.\n\n\n\nThis program can be used for an image dataset capture with an Image Classification project.\n\nInspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial: ESP32-CAM Take Photo and Display in Web Server, which I invite all of you to visit."
+ "text": "4.4.8 Testing WiFi\nOne of the differentiators of the XIAO ESP32S3 is its WiFi capability. So, let’s test its radio, scanning the wifi networks around it. You can do it by running one of the code examples on the board.\nGo to Arduino IDE Examples and look for WiFI ==> WiFIScan\nOn the Serial monitor, you should see the wifi networks (SSIDs and RSSIs) in the range of your device. Here is what I got on the lab:\n\nSimple WiFi Server (Turning LED ON/OFF)\nLet’s test the device’s capability to behave as a WiFi Server. We will host a simple page on the device that sends commands to turn the XIAO built-in LED ON and OFF.\nLike before, go to GitHub to download the folder with the sketch: SimpleWiFiServer.\nBefore running the sketch, you should enter your network credentials:\nconst char* ssid = \"Your credentials here\";\nconst char* password = \"Your credentials here\";\nYou can monitor how your server is working with the Serial Monitor.\n\nTake the IP address and enter it on your browser:\n\nYou will see a page with links that can turn ON and OFF the built-in LED of your XIAO.\nStreaming video to Web\nNow that you know that you can send commands from the webpage to your device, let’s do the reverse. Let’s take the image captured by the camera and stream it to a webpage:\nDownload from GitHub the folder that contains the code: XIAO-ESP32S3-Streeming_Video.ino.\n\nRemember that the folder contains not only the.ino file, but also a couple of.h files, necessary to handle the camera.\n\nEnter your credentials and run the sketch. On the Serial monitor, you can find the page address to enter in your browser:\n\nOpen the page on your browser (wait a few seconds to start the streaming). That’s it.\n\nStreamlining what your camera is “seen” can be important when you position it to capture a dataset for an ML project (for example, using the code “take_phots_commands.ino”.\nOf course, we can do both things simultaneously, show what the camera is seeing on the page, and send a command to capture and save the image on the SD card. For that, you can use the code Camera_HTTP_Server_STA which folder can be downloaded from GitHub.\n\nThe program will do the following tasks:\n\nSet the camera to JPEG output mode.\nCreate a web page (for example ==> http://192.168.4.119//). The correct address will be displayed on the Serial Monitor.\nIf server.on (“/capture”, HTTP_GET, serverCapture), the program takes a photo and sends it to the Web.\nIt is possible to rotate the image on webPage using the button [ROTATE]\nThe command [CAPTURE] only will preview the image on the webpage, showing its size on Serial Monitor\nThe [SAVE] command will save an image on the SD Card, also showing the image on the web.\nSaved images will follow a sequential naming (image1.jpg, image2.jpg.\n\n\n\nThis program can be used for an image dataset capture with an Image Classification project.\n\nInspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial: ESP32-CAM Take Photo and Display in Web Server, which I invite all of you to visit.\nUsing the CameraWebServer\nIn File \\> Examples \\> ESP32 \\> Camera, select CameraWebServer\nYou also should comment on all cameras’ models, except the XIAO model pins:\n#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM\nand do not forget the Tools to enable the PSRAM.\nEnter your wifi credentials and upload the code to the device:\n\nIf the code is executed correctly, you should see the address on the Serial Monitor:\n\n\n\nimage-20240214163034559\n\n\nCopy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select [START STREAM]. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the [Save] button.\n\nThat’s it! You can save the images directly in your computer to be used on projects."
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- "section": "4.4.9 Fruits versus Veggies - A TinyML Image Classification project",
- "text": "4.4.9 Fruits versus Veggies - A TinyML Image Classification project\n\nNow that we have an embedded camera running, it is time to try image classification. For comparative motive, we will replicate the same image classification project developed to be used with an old ESP2-CAM:\nESP32-CAM: TinyML Image Classification - Fruits vs Veggies\n\nThe whole idea of our project will be training a model and proceeding with inference on the XIAO ESP32S3 Sense. For training, we should find some data (in fact, tons of data!).\nBut first of all, we need a goal! What do we want to classify?\nWith TinyML, a set of technics associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory in this situation). We will differentiate apples from bananas and potatoes (you can try other categories).\nSo, let’s find a specific dataset that includes images from those categories. Kaggle is a good start:\nhttps://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition\nThis dataset contains images of the following food items:\n\nFruits - banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango.\nVegetables - cucumber, carrot, capsicum, onion, potato, lemon, tomato, radish, beetroot, cabbage, lettuce, spinach, soybean, cauliflower, bell pepper, chili pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant.\n\nEach category is split into the train (100 images), test (10 images), and validation (10 images).\n\nDownload the dataset from the Kaggle website to your computer.\n\n\nOptionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the sketch discussed in the last section."
+ "section": "4.4.9 Fruits versus Veggies - A TinyML Image Classification Project",
+ "text": "4.4.9 Fruits versus Veggies - A TinyML Image Classification Project\n\nNow that we have an embedded camera running, it is time to try image classification. For comparative motive, we will replicate the same image classification project developed to be used with an old ESP2-CAM:\nESP32-CAM: TinyML Image Classification - Fruits vs Veggies\n\nThe whole idea of our project will be to train a model and proceed with inference on the XIAO ESP32S3 Sense. For training, we should find some data (in fact, tons of data!).\nBut first of all, we need a goal! What do we want to classify?\nWith TinyML, a set of techniques associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory). We will differentiate apples from bananas and potatoes (you can try other categories).\nSo, let’s find a specific dataset that includes images from those categories. Kaggle is a good start:\nhttps://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition\nThis dataset contains images of the following food items:\n\nFruits - banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango.\nVegetables - cucumber, carrot, capsicum, onion, potato, lemon, tomato, radish, beetroot, cabbage, lettuce, spinach, soybean, cauliflower, bell pepper, chili pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant.\n\nEach category is split into the train (100 images), test (10 images), and validation (10 images).\n\nDownload the dataset from the Kaggle website to your computer.\n\n\nOptionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the codes discussed in the last section."
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"section": "4.4.10 Training the model with Edge Impulse Studio",
- "text": "4.4.10 Training the model with Edge Impulse Studio\nWe will use the Edge Impulse Studio for training our model. Edge Impulse is a leading development platform for machine learning on edge devices.\nEnter your account credentials (or create a free account) at Edge Impulse. Next, create a new project:\n\nData Acquisition\nNext, on the UPLOAD DATA section, upload from your computer the files from chosen categories:\n\nYou should now have your training dataset, split in three classes of data:\n\n\nYou can upload extra data for further model testing or split the training data. I will leave as it, to use most data possible.\n\nImpulse Design\nAn impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data.\nAs mentioned, classifying images is the most common use of Deep Learning, but much data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousand of images to “teach or model” to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We called this technic “Transfer Learning” (TL).\n\nWith TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case).\nSo, starting from the raw images, we will resize them (96x96) pixels and so, feeding them to our Transfer Learning block:\n\nPre-processing (Feature generation)\nBesides resizing the images, we should change them to Grayscale instead to keep the actual RGB color depth. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.\n\nDo not forget to “Save parameters.” This will generate the features to be used in training.\nTraining (Transfer Learning & Data Augmentation)\nIn 2007, Google introduced MobileNetV1,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.\nAlthough 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. MobileNet introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller and less computationally expensive models. The role of the width multiplier α is to thin a network uniformly at each layer.\nEdge Impulse Studio has available MobileNet V1 (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 highest the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be needed to run the model and imply more latency.\nThe smaller footprint will be obtained at another extreme with MobileNet V1 and α=0.10 (around 53.2K RAM and 101K ROM).\nWhen we first published this project to be running on an ESP32-CAM, we stayed at the lower side of possibilities which guaranteed the inference with small latency but not with high accuracy. For this first pass, we will keep this model design (MobileNet V1 and α=0.10).\nAnother important technic 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).\nUnder the rood, here you can see how Edge Impulse implements a data Augmentation policy on your data:\n# Implements the data augmentation policy\ndef augment_image(image, label):\n # Flips the image randomly\n image = tf.image.random_flip_left_right(image)\n\n # Increase the image size, then randomly crop it down to\n # the original dimensions\n resize_factor = random.uniform(1, 1.2)\n new_height = math.floor(resize_factor * INPUT_SHAPE[0])\n new_width = math.floor(resize_factor * INPUT_SHAPE[1])\n image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)\n image = tf.image.random_crop(image, size=INPUT_SHAPE)\n\n # Vary the brightness of the image\n image = tf.image.random_brightness(image, max_delta=0.2)\n\n return image, label\nExposure 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.\nThe final layer of our model will have 16 neurons with a 10% of dropout for overfitting prevention. Here is the Training output:\n\nThe result is not great. The model reached around 77% of accuracy, but the amount of RAM expected to be used during the inference is relatively small (around 60 KBytes), which is very good.\nDeployment\nThe trained model will be deployed as a.zip Arduino library:\n\nOpen your Arduino IDE, and under Sketch, go to Include Library and add.ZIP Library. Select the file you download from Edge Impulse Studio, and that’s it!\n\nUnder the Examples tab on Arduino IDE, you should find a sketch code under your project name.\n\nOpen the Static Buffer example:\n\nYou can see that the first line of code is exactly the calling of a library with all the necessary stuff for running inference on your device.\n#include <XIAO-ESP32S3-CAM-Fruits-vs-Veggies_inferencing.h>\nOf course, this is a generic code (a “template”), that only gets one sample of raw data (stored on the variable: features = {} and run the classifier, doing the inference. The result is shown on Serial Monitor.\nWe should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes.\n\nReturning to your project (Tab Image), copy one of the Raw Data Sample:\n\n9, 216 features will be copied to the clipboard. This is the input tensor (a flattened image of 96x96x1), in this case, bananas. Past this Input tensor on features[] = {0xb2d77b, 0xb5d687, 0xd8e8c0, 0xeaecba, 0xc2cf67, …}\n\nEdge Impulse included the library ESP NN in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (runing at Arduino IDE).\nNow, when running the inference, you should get, as a result, the highest score for “banana”.\n\nGreat news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. It is a better result than the ESP32 CAM (525ms of latency).\nNow, we should incorporate the camera and classify images in real-time.\nGo to the Arduino IDE Examples and download from your project the sketch esp32_camera:\n\nYou should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75:\n#define PWDN_GPIO_NUM -1 \n#define RESET_GPIO_NUM -1 \n#define XCLK_GPIO_NUM 10 \n#define SIOD_GPIO_NUM 40 \n#define SIOC_GPIO_NUM 39\n#define Y9_GPIO_NUM 48 \n#define Y8_GPIO_NUM 11 \n#define Y7_GPIO_NUM 12 \n#define Y6_GPIO_NUM 14 \n#define Y5_GPIO_NUM 16 \n#define Y4_GPIO_NUM 18 \n#define Y3_GPIO_NUM 17 \n#define Y2_GPIO_NUM 15 \n#define VSYNC_GPIO_NUM 38 \n#define HREF_GPIO_NUM 47 \n#define PCLK_GPIO_NUM 13\nHere you can see the resulting code:\n\nThe modified sketch can be downloaded from GitHub: xiao_esp32s3_camera.\n\nNote that you can optionally keep the pins as a.h file as we did on previous sections.\n\nUpload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor."
+ "text": "4.4.10 Training the model with Edge Impulse Studio\nWe will use the Edge Impulse Studio to train our model. As you know, Edge Impulse is a leading development platform for machine learning on edge devices.\nEnter your account credentials (or create a free account) at Edge Impulse. Next, create a new project:\n\nData Acquisition\nNext, on the UPLOAD DATA section, upload from your computer the files from chosen categories:\n\nYou should now have your training dataset split in three classes of data:\n\n\nYou can upload extra data for further model testing or split the training data. I will leave it as it is to use the most data possible.\n\nImpulse Design\n\nAn impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data.\n\nClassifying images is the most common use of deep learning, but a lot of data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousands of images to “teach or model” to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We call this technique “Transfer Learning” (TL).\n\nWith TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case).\nSo, starting from the raw images, we will resize them (96x96) pixels and feed them to our Transfer Learning block:\n\nPre-processing (Feature generation)\nBesides resizing the images, we can change them to Grayscale or keep the actual RGB color depth. Let’s start selecting Grayscale. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference.\n\nDo not forget to [Save parameters].” This will generate the features to be used in training.\nTraining (Transfer Learning & Data Augmentation)\nIn 2007, Google introduced MobileNetV1, 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.\nAlthough 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. MobileNet 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.\nEdge Impulse Studio has available MobileNet V1 (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.\nThe smaller footprint will be obtained at another extreme with MobileNet V1 and α=0.10 (around 53.2K RAM and 101K ROM).\nWhen we first published this project to be running on an ESP32-CAM, we stayed at the lower side of possibilities which guaranteed the inference with small latency but not with high accuracy. For this first pass, we will keep this model design (MobileNet V1 and α=0.10).\nAnother necessary technique to use 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).\nUnder the rood, here you can see how Edge Impulse implements a data Augmentation policy on your data:\n# Implements the data augmentation policy\ndef augment_image(image, label):\n # Flips the image randomly\n image = tf.image.random_flip_left_right(image)\n\n # Increase the image size, then randomly crop it down to\n # the original dimensions\n resize_factor = random.uniform(1, 1.2)\n new_height = math.floor(resize_factor * INPUT_SHAPE[0])\n new_width = math.floor(resize_factor * INPUT_SHAPE[1])\n image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)\n image = tf.image.random_crop(image, size=INPUT_SHAPE)\n\n # Vary the brightness of the image\n image = tf.image.random_brightness(image, max_delta=0.2)\n\n return image, label\nExposure 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.\nThe final layer of our model will have 16 neurons with a 10% of dropout for overfitting prevention. Here is the Training output:\n\nThe result is not great. The model reached around 77% of accuracy, but the amount of RAM expected to be used during the inference is relatively small (around 60 KBytes), which is very good.\nDeployment\nThe trained model will be deployed as a .zip Arduino library:\n\nOpen your Arduino IDE, and under Sketch, go to Include Library and add.ZIP Library. Select the file you download from Edge Impulse Studio, and that’s it!\n\nUnder the Examples tab on Arduino IDE, you should find a sketch code under your project name.\n\nOpen the Static Buffer example:\n\nYou can see that the first line of code is exactly the calling of a library with all the necessary stuff for running inference on your device.\n#include <XIAO-ESP32S3-CAM-Fruits-vs-Veggies_inferencing.h>\nOf course, this is a generic code (a “template”) that only gets one sample of raw data (stored on the variable: features = {} and runs the classifier, doing the inference. The result is shown on the Serial Monitor.\nWe should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes.\n\nReturning to your project (Tab Image), copy one of the Raw Data Sample:\n\n9, 216 features will be copied to the clipboard. This is the input tensor (a flattened image of 96x96x1), in this case, bananas. Past this Input tensor on features[] = {0xb2d77b, 0xb5d687, 0xd8e8c0, 0xeaecba, 0xc2cf67, …}\n\nEdge Impulse included the library ESP NN in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (running at Arduino IDE).\nNow, when running the inference, you should get the highest score for “banana.”\n\nGreat news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. It is a better result than the ESP32 CAM (525ms of latency).\nNow, we should incorporate the camera and classify images in real time.\nGo to the Arduino IDE Examples and download from your project the sketch esp32_camera:\n\nYou should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75:\n#define PWDN_GPIO_NUM -1 \n#define RESET_GPIO_NUM -1 \n#define XCLK_GPIO_NUM 10 \n#define SIOD_GPIO_NUM 40 \n#define SIOC_GPIO_NUM 39\n#define Y9_GPIO_NUM 48 \n#define Y8_GPIO_NUM 11 \n#define Y7_GPIO_NUM 12 \n#define Y6_GPIO_NUM 14 \n#define Y5_GPIO_NUM 16 \n#define Y4_GPIO_NUM 18 \n#define Y3_GPIO_NUM 17 \n#define Y2_GPIO_NUM 15 \n#define VSYNC_GPIO_NUM 38 \n#define HREF_GPIO_NUM 47 \n#define PCLK_GPIO_NUM 13\nHere you can see the resulting code:\n\nThe modified sketch can be downloaded from GitHub: xiao_esp32s3_camera.\n\nNote that you can optionally keep the pins as a .h file as we did in previous sections.\n\nUpload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor."
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- "text": "4.4.12 Testing with a bigger model\nNow, let’s go to the other side of the model size. Let’s select a MobilinetV2 96x96 0.35, having as input RGB images.\n\nEven with a bigger model, the accuracy is not good, and worst, the amount of memory necessary to run the model increases five times, with latency increasing seven times (note that the performance here is estimated with a smaller device, the ESP-EYE. So, the real inference with the ESP32S3 should be better).\n\nTo make our model better, we will probably need more images to be trained.\n\nEven though our model did not improve in terms of accuracy, let’s test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch.\nLet’s redeploy the model. If the EON Compiler is enabled when you generate the library, the total memory needed for inference should be reduced, but it has no influence on accuracy.\n\nDoing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the latency was of 219ms, what it is great for such bigger model.\n\nIn our tests, this option works with MobileNet V2 but not V1. So, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the resultin accuraccy was higher.\n\n\nNote that the estimated latency for an Arduino Portenta (ou Nicla), running with a clock of 480MHz is 45ms.\n\nDeploying the model, I got an inference of only 135ms, remembering that the XIAO run with half of the clock used by the Portenta/Nicla (240MHz):"
+ "text": "4.4.12 Testing with a bigger model\nNow, let’s go to the other side of the model size. Let’s select a MobilinetV2 96x96 0.35, having as input RGB images.\n\nEven with a bigger model, the accuracy is not that good, and the amount of memory necessary to run the model increases five times, with latency increasing seven times\n\nNote that the performance here is estimated with a smaller device, the ESP-EYE. The actual inference with the ESP32S3 should be better.\n\nTo improve our model, we will need to train more images.\nEven though our model did not improve in terms of accuracy, let’s test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch.\nLet’s redeploy the model. If the EON Compiler is enabled when you generate the library, the total memory needed for inference should be reduced, but it has no influence on accuracy.\n\nDoing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the latency was of 219ms, what it is great for such bigger model.\n\nFor test, I trained the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the result in accuraccy was higher.\n\n\nNote that the estimated latency for an Arduino Portenta (ou Nicla), running with a clock of 480MHz is 45ms.\n\nDeploying the model, I got an inference of only 135ms, remembering that the XIAO run with half of the clock used by the Portenta/Nicla (240MHz):"
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+ "section": "4.4.13 Runing inference on the SenseCraft-Web-Toolkit",
+ "text": "4.4.13 Runing inference on the SenseCraft-Web-Toolkit\nOn big limitation of viewing inference on Arduino IDE, is that we can not see what the camera is really focusing. A good alternative for that is the SenseCraft-Web-Toolkit, a visual model deployment tool provided by SSCMA (Seeed SenseCraft Model Assistant). With this tool, you can easily deploy models to various platforms through simple operations. The tool provides a user-friendly interface and does not require any coding.\nFollow the following steps to start the SenseCraft-Web-Toolkit:\n\nOpen the SenseCraft-Web-Toolkit website.\nConnect the XIAO to your computer:\n\n\nHaving the XIAO connected, select it as below:\n\n\n\nSelect the device/Port and press [Connect]:\n\n\n\nYou can try several Computer Vision models previously uploaded by Seeed Studio. Try them and have fun!\n\nIn our case, we will use the blue button at the botton of the page: [Upload Custom AI Model].\nBut first, we will need to download from Edge Impulse Studio, our quantized .tflite model.\n\nGo to your project at Edge Impulse Studio, or clone this one:\n\n\nXIAO-ESP32S3-CAM-Fruits-vs-Veggies-v1-ESP-NN\n\n\nOn Dashboard, download the model (“block output”): Transfer learning mdodel - TensorFlow Lite (int8 quantized)\n\n\n\nOn SenseCraft-Web-Toolkit, use the blue button at the botton of the page: [Upload Custom AI Model]. A window will pop-up. Enter with the Model file that you downloaded to your computer from Edge Impulse Studio, choos a Model Name and enter with labels (ID:Object):\n\n\n\nNote that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: apple, banana, potato).\n\nAfter a few seconds (or minutes), the model will be uploaded to your device and the camera image will appear in real-time on the Preview Sector:\n\nThe Classification result will be at at the top of the image. You can also select the Confidence of your inference cursor Confidence.\nClicking in the top button (Device Log), you can open a Serial Monitor to follow the inference, same that we have done with the Arduino IDE:\n\nOn Device Log, you will get Information as:\n\n\nPreprocess time (image capture and Crop): 4ms;\nInference time (model latency): 106ms,\nPostprocess time (display of the image and inclusion of data): 0ms.\nOutput tensor (classes), for example: [[89,0]]; where 0 is Apple (and 1is banana and 2 is potato)\n\nHere are other screen shots:"
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- "text": "4.4.13 Conclusion\nThe XIAO ESP32S3 Sense is a very flexible, not expensive, and easy-to-program device. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication.\nOn the GitHub repository, you will find the last version of the codes: XIAO-ESP32S3-Sense."
+ "section": "4.4.14 Conclusion",
+ "text": "4.4.14 Conclusion\nThe XIAO ESP32S3 Sense is a very flexible, not expensive, and easy-to-program device. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication.\nOn the GitHub repository, you will find the last version of the codes: XIAO-ESP32S3-Sense."
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"title": "4.5 Object Detection",
"section": "4.5.4 Data Collection",
- "text": "4.5.4 Data Collection\nYou can use the XIAO, your phone, or other devices for the image capture. Here, we will use the XIAO with a code in the ESP32 library.\n\n4.5.4.1 Collecting Dataset with the XIAO ESP32S3\nOpen the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On File \\> Examples \\> ESP32 \\> Camera, select CameraWebServer. On the BOARDS MANAGER panel, confirm that you have installed the latest “stable” package.\n\nThe current alpha versions (3.0) did not work correctly with the XIAO. The 2.0.14 works fine.\n\nYou also should comment on all cameras’ models, except the XIAO model pins:\n#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM\nand on Tools, enable the PSRAM. Enter your wifi credentials and upload the code to the device:\n\nIf the code is executed correctly, you should see the address on the Serial Monitor:\n\nCopy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select [START STREAM]. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the [Save] button.\n\nEdge impulse suggests that the objects should be of similar size and not overlapping for better performance. This is OK in an industrial facility, where the camera should be fixed, keeping the same distance from the objects to be detected. Despite that, we will also try using mixed sizes and positions to see the result.\n\nWe do not need to create separate folders for our images because each contains multiple labels.\n\nWe suggest around 50 images mixing the objects and varying the number of each appearing on the scene. Try to capture different angles, backgrounds, and light conditions.\n\nThe stored images use a QVGA frame size of 320x240 and RGB565 (color pixel format).\n\nAfter capturing your dataset, [Stop Stream] and move your images to a folder.\n\n\n4.5.4.2 Edge Impulse Studio\n\n4.5.4.2.1 Setup the project\nGo to Edge Impulse Studio, enter your credentials at Login (or create an account), and start a new project.\n\n\nHere, you can clone the project developed for this hands-on: XIAO-ESP32S3-Sense-Object_Detection\n\nOn your Project Dashboard, go down and on Project info and select Bounding boxes (object detection) and Espressif ESP-EYE (most similar to our board) as your Target Device:\n\n\n\n\n4.5.4.3 Uploading the unlabeled data\nOn Studio, go to the Data acquisition tab, and on the UPLOAD DATA section, upload files captured as a folder from your computer.\n\n\nYou can leave for the Studio to split your data automatically between Train and Test or do it manually. We will upload all of them as training.\n\n\nAll the not-labeled images (47) were uploaded but still need to be labeled appropriately before being used as a project dataset. The Studio has a tool for that purpose, which you can find in the link Labeling queue (47).\nThere are two ways you can use to perform AI-assisted labeling on the Edge Impulse Studio (free version):\n\nUsing yolov5\nTracking objects between frames\n\n\nEdge Impulse launched an auto-labeling feature for Enterprise customers, easing labeling tasks in object detection projects.\n\nOrdinary objects can quickly be identified and labeled using an existing library of pre-trained object detection models from YOLOv5 (trained with the COCO dataset). But since, in our case, the objects are not part of COCO datasets, we should select the option of tracking objects. With this option, once you draw bounding boxes and label the images in one frame, the objects will be tracked automatically from frame to frame, partially labeling the new ones (not all are correctly labeled).\n\nYou can use the EI uploader to import your data if you already have a labeled dataset containing bounding boxes.\n\n\n\n4.5.4.4 Labeling the Dataset\nStarting with the first image of your unlabeled data, use your mouse to drag a box around an object to add a label. Then click Save labels to advance to the next item.\n\nContinue with this process until the queue is empty. At the end, all images should have the objects labeled as those samples below:\n\nNext, review the labeled samples on the Data acquisition tab. If one of the labels is wrong, you can edit it using the three dots menu after the sample name:\n\nYou will be guided to replace the wrong label and correct the dataset.\n\n\n\n4.5.4.5 Balancing the dataset and split Train/Test\nAfter labeling all data, it was realized that the class fruit had many more samples than the bug. So, 11 new and additional bug images were collected (ending with 58 images). After labeling them, it is time to select some images and move them to the test dataset. You can do it using the three-dot menu after the image name. I selected six images, representing 13% of the total dataset."
+ "text": "4.5.4 Data Collection\nYou can use the XIAO, your phone, or other devices for the image capture. Here, we will use the XIAO with a code in the ESP32 library.\n\n4.5.4.1 Collecting Dataset with the XIAO ESP32S3\nOpen the Arduino IDE and select the XIAO_ESP32S3 board (and the port where it is connected). On File \\> Examples \\> ESP32 \\> Camera, select CameraWebServer.\nOn the BOARDS MANAGER panel, confirm that you have installed the latest “stable” package.\n\n⚠️ Attention\nAlpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead.\n\nYou also should comment on all cameras’ models, except the XIAO model pins:\n#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM\nand on Tools, enable the PSRAM. Enter your wifi credentials and upload the code to the device:\n\nIf the code is executed correctly, you should see the address on the Serial Monitor:\n\nCopy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select [START STREAM]. Wait for a few seconds/minutes, depending on your connection. You can save an image on your computer download area using the [Save] button.\n\nEdge impulse suggests that the objects should be of similar size and not overlapping for better performance. This is OK in an industrial facility, where the camera should be fixed, keeping the same distance from the objects to be detected. Despite that, we will also try using mixed sizes and positions to see the result.\n\nWe do not need to create separate folders for our images because each contains multiple labels.\n\nWe suggest around 50 images mixing the objects and varying the number of each appearing on the scene. Try to capture different angles, backgrounds, and light conditions.\n\nThe stored images use a QVGA frame size of 320x240 and RGB565 (color pixel format).\n\nAfter capturing your dataset, [Stop Stream] and move your images to a folder.\n\n\n4.5.4.2 Edge Impulse Studio\n\n4.5.4.2.1 Setup the project\nGo to Edge Impulse Studio, enter your credentials at Login (or create an account), and start a new project.\n\n\nHere, you can clone the project developed for this hands-on: XIAO-ESP32S3-Sense-Object_Detection\n\nOn your Project Dashboard, go down and on Project info and select Bounding boxes (object detection) and Espressif ESP-EYE (most similar to our board) as your Target Device:\n\n\n\n\n4.5.4.3 Uploading the unlabeled data\nOn Studio, go to the Data acquisition tab, and on the UPLOAD DATA section, upload files captured as a folder from your computer.\n\n\nYou can leave for the Studio to split your data automatically between Train and Test or do it manually. We will upload all of them as training.\n\n\nAll the not-labeled images (47) were uploaded but still need to be labeled appropriately before being used as a project dataset. The Studio has a tool for that purpose, which you can find in the link Labeling queue (47).\nThere are two ways you can use to perform AI-assisted labeling on the Edge Impulse Studio (free version):\n\nUsing yolov5\nTracking objects between frames\n\n\nEdge Impulse launched an auto-labeling feature for Enterprise customers, easing labeling tasks in object detection projects.\n\nOrdinary objects can quickly be identified and labeled using an existing library of pre-trained object detection models from YOLOv5 (trained with the COCO dataset). But since, in our case, the objects are not part of COCO datasets, we should select the option of tracking objects. With this option, once you draw bounding boxes and label the images in one frame, the objects will be tracked automatically from frame to frame, partially labeling the new ones (not all are correctly labeled).\n\nYou can use the EI uploader to import your data if you already have a labeled dataset containing bounding boxes.\n\n\n\n4.5.4.4 Labeling the Dataset\nStarting with the first image of your unlabeled data, use your mouse to drag a box around an object to add a label. Then click Save labels to advance to the next item.\n\nContinue with this process until the queue is empty. At the end, all images should have the objects labeled as those samples below:\n\nNext, review the labeled samples on the Data acquisition tab. If one of the labels is wrong, you can edit it using the three dots menu after the sample name:\n\nYou will be guided to replace the wrong label and correct the dataset.\n\n\n\n4.5.4.5 Balancing the dataset and split Train/Test\nAfter labeling all data, it was realized that the class fruit had many more samples than the bug. So, 11 new and additional bug images were collected (ending with 58 images). After labeling them, it is time to select some images and move them to the test dataset. You can do it using the three-dot menu after the image name. I selected six images, representing 13% of the total dataset."
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