From 663c3d0b7576e6e7a56af7e32bf245731f369bb1 Mon Sep 17 00:00:00 2001 From: Marcelo Rovai Date: Wed, 14 Feb 2024 19:16:45 -0300 Subject: [PATCH] Update 4-5 -Object Detection Including Deploying the Model (SenseCraft-Web-Toolkit) section --- chapter_4-4.qmd | 2 +- chapter_4-5.qmd | 64 +++++++++++++++++++++++++++++- docs/chapter_4-4.html | 2 +- docs/chapter_4-5.html | 67 +++++++++++++++++++++++++++++--- docs/imgs-4-5/monitor.png | Bin 0 -> 40478 bytes docs/imgs-4-5/sense-craft-1.jpg | Bin 0 -> 675947 bytes docs/imgs-4-5/sense-craft-2.jpg | Bin 0 -> 62824 bytes docs/imgs-4-5/sense-craft-3.jpg | Bin 0 -> 99496 bytes docs/imgs-4-5/sense-craft-4.jpg | Bin 0 -> 296370 bytes docs/imgs_4-4/senseCraft-3.jpg | Bin 0 -> 60314 bytes docs/search.json | 19 ++++++--- imgs-4-5/monitor.png | Bin 0 -> 40478 bytes imgs-4-5/sense-craft-1.jpg | Bin 0 -> 675947 bytes imgs-4-5/sense-craft-2.jpg | Bin 0 -> 62824 bytes imgs-4-5/sense-craft-3.jpg | Bin 0 -> 99496 bytes imgs-4-5/sense-craft-4.jpg | Bin 0 -> 296370 bytes 16 files changed, 139 insertions(+), 15 deletions(-) create mode 100644 docs/imgs-4-5/monitor.png create mode 100644 docs/imgs-4-5/sense-craft-1.jpg create mode 100644 docs/imgs-4-5/sense-craft-2.jpg create mode 100644 docs/imgs-4-5/sense-craft-3.jpg create mode 100644 docs/imgs-4-5/sense-craft-4.jpg create mode 100644 docs/imgs_4-4/senseCraft-3.jpg create mode 100644 imgs-4-5/monitor.png create mode 100644 imgs-4-5/sense-craft-1.jpg create mode 100644 imgs-4-5/sense-craft-2.jpg create mode 100644 imgs-4-5/sense-craft-3.jpg create mode 100644 imgs-4-5/sense-craft-4.jpg diff --git a/chapter_4-4.qmd b/chapter_4-4.qmd index 9597325..df22f5e 100644 --- a/chapter_4-4.qmd +++ b/chapter_4-4.qmd @@ -530,7 +530,7 @@ But first, we will need to download from Edge Impulse Studio, our **quantized .t 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): -![](/Users/marcelo_rovai/Documents/GitHub/XIAO_Big_Power_Small_Board-ebook/imgs_4-4/senseCraft-3.jpg) +![](imgs_4-4/senseCraft-3.jpg) > Note that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: apple, banana, potato). diff --git a/chapter_4-5.qmd b/chapter_4-5.qmd index 0a7e33c..5e89368 100644 --- a/chapter_4-5.qmd +++ b/chapter_4-5.qmd @@ -239,7 +239,7 @@ Once connected, you can use the smartphone to capture actual images to be tested One thing to be noted is that the model can produce false positives and negatives. This can be minimized by defining a proper Confidence Threshold (use the Three dots menu for the setup). Try with 0.8 or more. -## 4.5.7 Deploying the Model +## 4.5.7 Deploying the Model (Arduino IDE) Select the Arduino Library and Quantized (int8) model, enable the EON Compiler on the Deploy Tab, and press \[Build\]. @@ -294,7 +294,67 @@ Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start detect Note that the model latency is 143ms, and the frame rate per second is around 7 fps (similar to what we got with the Image Classification project). This happens because FOMO is cleverly built over a CNN model, not with an object detection model like the SSD MobileNet. For example, when running a MobileNetV2 SSD FPN-Lite 320x320 model on a Raspberry Pi 4, the latency is around five times higher (around 1.5 fps). -## 4.5.8 Conclusion +## 4.5.8 Deploying the Model (SenseCraft-Web-Toolkit) + +Same as we disscussed on the Image Classification chapter, verifying inference with Image models on Arduino IDE is very challenging, because we can not see what the camera is really focusing. Again, let's use the **SenseCraft-Web-Toolkit**. + +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: + +![](imgs_4-4/senseCraft-1.jpg) + +- Select the device/Port and press `[Connect]`: + +![](imgs_4-4/senseCraft-2.jpg) + +> 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"): `Object Detection model - TensorFlow Lite (int8 quantized)` + +![](imgs-4-5/sense-craft-1.jpg) + +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, choose a Model Name and enter with labels (ID:Object): + +![](imgs-4-5/sense-craft-2.jpg) + +> Note that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: background, bug, fruit). + +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: + +![](imgs-4-5/sense-craft-3.jpg) + +The detect objects will be marked (the centroid). You can select the Confidence of your inference cursor `Confidence`. and `IoU`, which is used to assess the accuracy of predicted bounding boxes compared to truth bounding boxes + +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. + +![](imgs-4-5/monitor.png) + +On Device Log, you will get Information as: + +- Preprocess time (image capture and Crop): 3ms; +- Inference time (model latency): 115ms, +- Postprocess time (display of the image and marking objects): 1ms. +- Output tensor (boxes), for example one of the boxes: [[30,150, 20, 20,97, 2]]; where 30,150, 20, 20 are the coordenates of the box (around the centroid); 97 is the inference result and 2 is the class (in this case 2: fruit) + +> Note that in the above example, we got 5 boxes, because une of the fruit got 3 centroids. One soluction will be doing a post-processing where we can aggregate close centroids in one. + +Here are other screen shots: + +![](imgs-4-5/sense-craft-4.jpg) + +## 4.5.9 Conclusion FOMO is a significant leap in the image processing space, as Louis Moreau and Mat Kelcey put it during its launch in 2022: diff --git a/docs/chapter_4-4.html b/docs/chapter_4-4.html index 4ad7ee5..fe1ae64 100644 --- a/docs/chapter_4-4.html +++ b/docs/chapter_4-4.html @@ -798,7 +798,7 @@

  • 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).

    diff --git a/docs/chapter_4-5.html b/docs/chapter_4-5.html index c91d979..879991e 100644 --- a/docs/chapter_4-5.html +++ b/docs/chapter_4-5.html @@ -401,8 +401,9 @@

    Table of contents

    -
  • 4.5.7 Deploying the Model
  • -
  • 4.5.8 Conclusion
  • +
  • 4.5.7 Deploying the Model (Arduino IDE)
  • +
  • 4.5.8 Deploying the Model (SenseCraft-Web-Toolkit)
  • +
  • 4.5.9 Conclusion
  • @@ -619,8 +620,8 @@

    4.5.6.

    One thing to be noted is that the model can produce false positives and negatives. This can be minimized by defining a proper Confidence Threshold (use the Three dots menu for the setup). Try with 0.8 or more.

    -
    -

    4.5.7 Deploying the Model

    +
    +

    4.5.7 Deploying the Model (Arduino IDE)

    Select the Arduino Library and Quantized (int8) model, enable the EON Compiler on the Deploy Tab, and press [Build].

    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!

    @@ -655,8 +656,64 @@

    4.5.7 Deploying the Mo

    Note that the model latency is 143ms, and the frame rate per second is around 7 fps (similar to what we got with the Image Classification project). This happens because FOMO is cleverly built over a CNN model, not with an object detection model like the SSD MobileNet. For example, when running a MobileNetV2 SSD FPN-Lite 320x320 model on a Raspberry Pi 4, the latency is around five times higher (around 1.5 fps).

    +
    +

    4.5.8 Deploying the Model (SenseCraft-Web-Toolkit)

    +

    Same as we disscussed on the Image Classification chapter, verifying inference with Image models on Arduino IDE is very challenging, because we can not see what the camera is really focusing. Again, let’s use the SenseCraft-Web-Toolkit.

    +

    Follow the following steps to start the SenseCraft-Web-Toolkit:

    +
      +
    1. Open the SenseCraft-Web-Toolkit website.
    2. +
    3. Connect the XIAO to your computer:
    4. +
    +
      +
    • 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.

    +
      +
    1. Go to your project at Edge Impulse Studio, or clone this one:
    2. +
    + +
      +
    1. On Dashboard, download the model (“block output”): Object Detection model - TensorFlow Lite (int8 quantized)
    2. +
    +

    +
      +
    1. 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, choose a Model Name and enter with labels (ID:Object):
    2. +
    +

    +
    +

    Note that you should use the labels trained on EI Studio, entering them at alphabetic order (in our case: background, bug, fruit).

    +
    +

    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 detect objects will be marked (the centroid). You can select the Confidence of your inference cursor Confidence. and IoU, which is used to assess the accuracy of predicted bounding boxes compared to truth bounding boxes

    +

    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): 3ms;
    • +
    • Inference time (model latency): 115ms,
    • +
    • Postprocess time (display of the image and marking objects): 1ms.
    • +
    • Output tensor (boxes), for example one of the boxes: [[30,150, 20, 20,97, 2]]; where 30,150, 20, 20 are the coordenates of the box (around the centroid); 97 is the inference result and 2 is the class (in this case 2: fruit)
    • +
    +
    +

    Note that in the above example, we got 5 boxes, because une of the fruit got 3 centroids. One soluction will be doing a post-processing where we can aggregate close centroids in one.

    +
    +

    Here are other screen shots:

    +

    +
    -

    4.5.8 Conclusion

    +

    4.5.9 Conclusion

    FOMO is a significant leap in the image processing space, as Louis Moreau and Mat Kelcey put it during its launch in 2022:

    FOMO is a ground-breaking algorithm that brings real-time object detection, tracking, and counting to microcontrollers for the first time.

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