Selecting an ML Model for Real-World Projects

Learn how to select the best ML model for a real-world project: defining the problem, identifying potential models, and choosing the optimal solution.

00:00:00 Learn how to select an ML model for a real-world project in steps: define the problem, identify potential models, and choose the best solution. Example: detecting intruders in a garden using tensorflow.js.

🔍 Selecting a machine learning model for a real-world project involves breaking down the problem, identifying potential models, and choosing the best solution.

💡 In a scenario where a web app needs to detect intruders without sending images to the cloud, tensorflow.js client-side in a browser is a suitable option.

📷 To detect intruders, the system should be able to identify humans in video frames, excluding cats, dogs, or other animals.

00:01:47 Exploring options for selecting an ML model: vision/human body models detect people, sound detects talking. Consider image classification, object detection, body segmentation, pose estimation, face/hand landmark detection. Inference speed is crucial for real-time applications.

🔎 There are various pre-trained models that can be used to detect a person in an image, including face and hand models.

🔊 Sound can also be used to detect talking, even if the person is not visible.

🤔 Different models like image classification, object detection, body segmentation, pose estimation, face landmark detection, and hand pose estimation have pros and cons that should be considered.

⏱️ The inference speed of the model, measured in milliseconds or frames per second, is important for real-time applications.

00:03:35 How to select an ML model for optimal performance and user experience based on frame rate, memory usage, and device specifications.

🔍 When selecting an ML model, consider the classification time and the amount of memory used.

💻 Knowing your end users' expected working environment is important in deciding which models are suitable.

📱 Consider the device and internet connectivity when choosing a machine learning model.

00:05:22 Learn how to benchmark an ML model by recording execution time, frames per second, file size, and RAM usage using a simple website.

💡 You can benchmark the ML model yourself by creating a simple website and recording execution time and file size.

⏱️ To calculate execution time, record a timestamp before and after executing the model and calculate the difference.

💾 To check file size, use the Chrome developer tools and inspect the network tab to see the assets being loaded.

00:07:10 Selecting an ML model based on runtime performance, file size, and RAM usage for a mobile device use case.

📊 The memory usage of ML models should be considered before selecting one.

⏱️ The frames per second (FPS) performance of ML models is important for real-time applications.

💾 File size may be less of an issue if the ML model is loaded once and the device has fast Wi-Fi.

🔍 Further research is required to explore faster body segmentation models.

💡 High RAM usage of face landmarks and hand pose models should be considered for older mobile devices.

00:08:57 The video discusses selecting an ML model to use for a specific use case. It compares image classification, object detection, and pose estimation models, highlighting the benefits and limitations of each. Ultimately, object detection is chosen due to its ability to detect multiple objects with fast performance and low RAM usage.

🔍 It is necessary to try different machine learning models to find the most suitable one for the intended use case.

🤖 Object detection provides more information with fast performance and lower RAM usage compared to image classification.

👥 Pose estimation can detect a maximum of six people at a time, but may not scale well for larger groups.

00:10:44 Learn how to select the best machine learning model for your needs and create a smart camera project from scratch. Speed and granularity considerations are discussed.

🤔 Choosing between pose estimation and object detection models depends on the need for speed and granularity.

🔎 Determining the best model requires investigation and research based on customer needs and the environment.

📷 The next section will guide you in creating a smart camera project using the selected object detection model.

Summary of a video "3.2: Selecting an ML model to use" by Google for Developers on YouTube.

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