You can implement GAN evaluation metrics like Inception Score IS or Fr chet Inception Distance FID by referring below:
- Set Up Pre-trained Inception Model: You can load a pre-trained Inception model (usually InceptionV3) to extract features from generated and real images.
In the above code, with the help of inception_model, we are extracting features.
- Calculate Inception Score (IS): You can generate images, feed them to the Inception model, and use the softmax output to compute the KL divergence for each class, averaging across images.
In the above code snippet, you can pass images through the model, calculate the probability, and then calculate KL divergence.
- Calculate Fréchet Inception Distance (FID): You can also extract features from real and generated images and compute the mean and covariance difference between the two distributions.
In the code snippet above, we calculate FID by measuring the mean and convergence between real and generated image features.
- Run and Interpret Metrics: Higher IS implies more diversity and realism; lower FID indicates closer similarity to real images.
By referring to the code above, you can implement GAN evaluation metrics like Inception Score IS or Fr chet Inception Distance FID.
Learn the critical preprocessing steps to improve the quality and realism of GAN generated images effectively.
