Practical insights for a data-driven approach to model optimization. By David Martin.
The author emphasizes that data is fundamental for successful machine learning models, often overlooked compared to complex model architecture. Drawing from experience building image classification systems, particularly one identifying over 1,500 zoo animal classes with high accuracy, they stress the critical need for “good” and “correct” training data.
Good training data requires:
- Subject Clarity: Animals must be clearly visible and identifiable (front and center), avoiding obscured features or multiple subjects. Ensure key distinguishing characteristics are prominent.
- Correct Labels: Labels must accurately reflect the image content, especially since even subject matter experts can err. The ML engineer plays a crucial role in label quality assurance.
Handling bad data is essential – images that don’t clearly show the main object (like an open field with a zebra) or contain errors should be removed or flagged as “Unknown”.
Pragmatic strategies include: * Using synthetic image augmentation techniques early, like zooming to capture detail. * Temporarily merging similar classes during development if data is sparse for one species, accepting the trade-off of generic identification. * Bulk label generation by models can speed up labelling, even with less-perfect models.
These practices form the bedrock of a reliable ML application. The next part will focus on creating specific datasets and evaluating the model effectively in production. Nice one!
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