Evaluating image generation: FID, CLIP score and human preference

How do you know if your image generation model is getting better? Pixel-by-pixel comparison is meaningless — two different high-quality images of a cat are equally valid. Image evaluation requires metrics that capture what humans care about: realism, diversity, and how well the image matches the text prompt. This lesson covers the three dominant metrics and why none of them alone is sufficient.

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