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Merge branch 'dev' into 471-go-through-all-figures
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profvjreddi committed Oct 30, 2024
2 parents c52763d + 45c0b2b commit 3179ecd
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256 changes: 128 additions & 128 deletions .all-contributorsrc
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64 changes: 32 additions & 32 deletions README.md
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64 changes: 32 additions & 32 deletions contents/contributors.qmd
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6 changes: 3 additions & 3 deletions contents/labs/arduino/nicla_vision/nicla_vision.qmd
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Expand Up @@ -6,9 +6,9 @@ These labs provide a unique opportunity to gain practical experience with machin

## Pre-requisites

- **Nicla Vision Board** : Ensure you have the Nicla Vision board.
- **USB Cable** : For connecting the board to your computer.
- **Network** : With internet access for downloading necessary software.
- **Nicla Vision Board**: Ensure you have the Nicla Vision board.
- **USB Cable**: For connecting the board to your computer.
- **Network**: With internet access for downloading necessary software.

## Setup

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16 changes: 8 additions & 8 deletions contents/labs/labs.qmd
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Expand Up @@ -41,28 +41,28 @@ These labs are designed for:
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+
| Keyword Spotting (KWS) | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | |
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+
| Image Classification | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | Coming soon. |
| Image Classification | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> |
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+
| Object Detection | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | Coming soon. |
| Object Detection | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> |
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+
| Motion Detection | <i class="bi bi-check2-square"></i> | <i class="bi bi-check2-square"></i> | |
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+
| Small Language Models (SLM)| | | Coming soon. |
| Small Language Models (SLM)| | | <i class="bi bi-check2-square"></i> |
+----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+

## Lab Structure

Each lab follows a structured approach:

1. **Introduction** : Explore the application and its significance in real-world scenarios.
1. **Introduction**: Explore the application and its significance in real-world scenarios.

2. **Setup** : Step-by-step instructions to configure the hardware and software environment.
2. **Setup**: Step-by-step instructions to configure the hardware and software environment.

3. **Deployment** : Guidance on training and deploying the pre-trained ML models on supported devices.
3. **Deployment**: Guidance on training and deploying the pre-trained ML models on supported devices.

4. **Exercises** : Hands-on tasks to modify and experiment with model parameters.
4. **Exercises**: Hands-on tasks to modify and experiment with model parameters.

5. **Discussion** : Analysis of results, potential improvements, and practical insights.
5. **Discussion**: Analysis of results, potential improvements, and practical insights.

## Troubleshooting and Support

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36 changes: 18 additions & 18 deletions contents/labs/raspi/image_classification/image_classification.qmd
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Expand Up @@ -777,19 +777,19 @@ This Python script creates a web-based interface for capturing and organizing im
#### Key Features:
1. **Web Interface** : Accessible from any device on the same network as the Raspberry Pi.
2. **Live Camera Preview** : This shows a real-time feed from the camera.
3. **Labeling System** : Allows users to input labels for different categories of images.
4. **Organized Storage** : Automatically saves images in label-specific subdirectories.
5. **Per-Label Counters** : Keeps track of how many images are captured for each label.
6. **Summary Statistics** : Provides a summary of captured images when stopping the capture process.
1. **Web Interface**: Accessible from any device on the same network as the Raspberry Pi.
2. **Live Camera Preview**: This shows a real-time feed from the camera.
3. **Labeling System**: Allows users to input labels for different categories of images.
4. **Organized Storage**: Automatically saves images in label-specific subdirectories.
5. **Per-Label Counters**: Keeps track of how many images are captured for each label.
6. **Summary Statistics**: Provides a summary of captured images when stopping the capture process.
#### Main Components:
1. **Flask Web Application** : Handles routing and serves the web interface.
2. **Picamera2 Integration** : Controls the Raspberry Pi camera.
3. **Threaded Frame Capture** : Ensures smooth live preview.
4. **File Management** : Organizes captured images into labeled directories.
1. **Flask Web Application**: Handles routing and serves the web interface.
2. **Picamera2 Integration**: Controls the Raspberry Pi camera.
3. **Threaded Frame Capture**: Ensures smooth live preview.
4. **File Management**: Organizes captured images into labeled directories.
#### Key Functions:
Expand Down Expand Up @@ -1435,10 +1435,10 @@ The code creates a web application for real-time image classification using a Ra
#### Key Components:
1. **Flask Web Application** : Serves the user interface and handles requests.
2. **PiCamera2** : Captures images from the Raspberry Pi camera module.
3. **TensorFlow Lite** : Runs the image classification model.
4. **Threading** : Manages concurrent operations for smooth performance.
1. **Flask Web Application**: Serves the user interface and handles requests.
2. **PiCamera2**: Captures images from the Raspberry Pi camera module.
3. **TensorFlow Lite**: Runs the image classification model.
4. **Threading**: Manages concurrent operations for smooth performance.
#### Main Features:
Expand Down Expand Up @@ -1491,10 +1491,10 @@ The code creates a web application for real-time image classification using a Ra
#### Key Concepts:
1. **Concurrent Operations** : Using threads to handle camera capture and classification separately from the web server.
2. **Real-time Updates** : Frequent updates to the classification results without page reloads.
3. **Model Reuse** : Loading the TFLite model once and reusing it for efficiency.
4. **Flexible Configuration** : Allowing users to adjust the confidence threshold on the fly.
1. **Concurrent Operations**: Using threads to handle camera capture and classification separately from the web server.
2. **Real-time Updates**: Frequent updates to the classification results without page reloads.
3. **Model Reuse**: Loading the TFLite model once and reusing it for efficiency.
4. **Flexible Configuration**: Allowing users to adjust the confidence threshold on the fly.
#### Usage:
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