How to Prevent Bias in Image Data Collection for Machine Learning

In the swiftly advancing field of machine learning, the caliber and variety of training data are vital for the effectiveness of models. In the context of image-based artificial intelligence, the quality of the dataset significantly influences the model's ability to generalize across various situations. A significant obstacle in the Data Collection Images data is bias, an often-overlooked issue that can result in unjust or inaccurate predictions. Bias within image data can lead to misclassification of objects by models, perpetuate stereotypes, and hinder performance in practical applications. I

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