In machine learning, the quality of your dataset is crucial for model performance. A study shows that even with models boasting billions of parameters, poor data quality will not yield high-quality results. When public datasets are inadequate for specific tasks, creating your own labeled dataset becomes necessary. Here’s how you can quickly build a simple annotation tool for image classification. This tool can help label your images efficiently, ensuring your model can generalize well to new, unseen data. The process involves using images from personal sources, like phone recordings, to classify and label data accurately.
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