Inpainting and outpainting in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Inpainting and outpainting

Which metric matters for Inpainting and Outpainting and WHY

For inpainting and outpainting, the key metrics focus on how well the model fills missing or extends image parts realistically and consistently. Common metrics include:

• PSNR (Peak Signal-to-Noise Ratio): Measures how close the generated pixels are to the original ones. Higher PSNR means less difference.
• SSIM (Structural Similarity Index): Checks if the generated image parts keep the same structure and texture as the original. Values closer to 1 are better.
• FID (Fréchet Inception Distance): Compares the distribution of generated images to real images. Lower FID means more realistic outputs.

These metrics matter because inpainting and outpainting need to produce visually plausible and coherent images, not just pixel-perfect copies.

Confusion Matrix or Equivalent Visualization

Inpainting and outpainting are image generation tasks, so confusion matrices do not apply directly. Instead, we use visual comparisons and similarity scores.

Example of PSNR, SSIM, and FID values for a test image:

    Original Image vs. Generated Image
    PSNR: 30.5 dB
    SSIM: 0.92
    FID: 18.3
    

Higher PSNR and SSIM indicate better quality. Lower FID means generated images are closer to real images.

Precision vs Recall Tradeoff (or Equivalent)

For inpainting and outpainting, the tradeoff is between:

• Fidelity: How closely the generated parts match the original image (measured by PSNR, SSIM).
• Creativity/Realism: How natural and plausible the generated parts look (measured by FID).

Example:

• A model with very high PSNR but poor FID might copy pixels too literally, causing unnatural patches.
• A model with low PSNR but good FID might create realistic but different textures, losing exact details.

Balancing these ensures the output is both accurate and visually pleasing.

What "Good" vs "Bad" Metric Values Look Like

Good values for inpainting/outpainting metrics typically are:

• PSNR: Above 25 dB is generally good; below 20 dB is poor.
• SSIM: Above 0.85 is good; below 0.7 is bad.
• FID: Lower is better; values below 30 are acceptable, below 10 are excellent.

Bad values mean the generated image parts look blurry, inconsistent, or unrealistic.

Common Pitfalls in Metrics

• Relying only on PSNR: High PSNR can happen if the model copies pixels but the output looks unnatural.
• Ignoring perceptual quality: Metrics like FID capture realism better than pixel-wise errors.
• Overfitting: Model might memorize training images, scoring well on metrics but failing on new images.
• Data leakage: Using test images during training inflates metric scores falsely.

Self-Check Question

Your inpainting model has a PSNR of 28 dB, SSIM of 0.9, but an FID of 45. Is it good for production? Why or why not?

Answer: No, because while PSNR and SSIM show good pixel similarity and structure, the high FID means the generated images are not very realistic overall. The model might produce images that look similar but unnatural. Improving realism is needed before production.

Key Result

Inpainting and outpainting models need balanced metrics: high PSNR and SSIM for accuracy, low FID for realism.