Data Collection
Data collection is one of the most important steps in building an efficent machine learning model. It is the process of creating new data or collecting relevant data from multiple sources for training the machine learning model. The performance of the machine learning model depends on the data on which it has been trained, so collecting qualitative data is crucial for developing a robust model.
How to collect data in Studio?
You can collect data using the following ways:
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Collect data using Graph UX: Graph UX allows you to collect real-time data from your local microphone or sensor on the development board. You can collect labeled as well as unlabeled data, as per your requirement.
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Bring your own Data: Studio provides the flexibility to import your existing datasets from your local hard drive, allowing you to leverage your already available data.
You can also collect data using Imagimob Capture Server to capture the real time data from any sensor or development board locally on your laptop or PC using a serial connection. To know how to capture the data using the Imagimob Capture Server, refer to the documentation (opens in a new tab).
Best practices
The following are some of the best practices for collecting data:
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Quality over Quantity: Prioritize collecting high-quality data over large quantities. As the saying goes, “garbage in, garbage out.” While large datasets are valuable, the accuracy and relevance of the data are paramount for building better models.
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Imbalanced data: In classification projects, ensure that you have balanced datasets. This means having an equal representation of each class to avoid bias and improve model performance.
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Diverse data: Collect diverse data to build a robust model as diversity in data helps the model generalize better and perform well across different scenarios.