What is the Inception Score (IS)?

Imagine you’re generating synthetic fashion designs using a GAN, and you want to assess whether your AI is producing realistic and varied outfits. How do you measure that—especially without human judgment? This is where the Inception Score (IS) becomes incredibly valuable. Widely used in evaluating Generative Adversarial Networks (GANs), IS quantifies how realistic and diverse your AI-generated images are.

Let’s explore how Inception Score works, its strengths and weaknesses, and how it compares to other evaluation metrics.

What is the inception score (IS)?

The Inception Score is a metric designed to evaluate the performance of generative models, especially GANs, by assessing:

• Image quality: How confident a classifier is in predicting the image class.

• Diversity: How many different classes are present in the generated samples.

It leverages a pre-trained Inception v3 classifier to estimate these two qualities without needing labeled data.

Next, let’s see how it actually works under the hood.

How does the inception score work?

The IS uses the following process:

• Pass generated images through a pretrained Inception v3 model.

• Collect the predicted class probability distribution $p(y|x)$.

• Compare it with the marginal distribution $p(y)$ across all images.

• Use KL divergence to compute:

IS=exp⁡(Ex[KL(p(y∣x)∣∣p(y))])IS = exp left( mathbb{E}_x [KL(p(y|x) || p(y))] right)IS=exp(Ex​[KL(p(y∣x)∣∣p(y))])

Intuition:

• If each image is clearly classifiable (low entropy $p(y|x)$), and

• The model generates a wide variety of classes (high entropy $p(y)$),

• Then the score will be high.

But no metric is perfect. Let’s look at IS limitations next.

What are the limitations of the inception score?

The limitations are as follows:

• Doesn’t compare to real data: IS measures internal quality, not how close the generated images are to real samples.

• Sensitive to mode collapse: A model might generate sharp but repetitive images and still get a high IS.

• Dataset-dependent: IS relies on the Inception model trained on ImageNet, which may not be suitable for non-natural images (e.g., medical scans).

To tackle these, researchers often compare IS with a more robust metric, FID.

Inception score vs. Fréchet inception distance

How to calculate the inception score?

To compute IS:

• Use a pretrained classifier (e.g., Inception v3).

• Predict probabilities for each image.

• Compute KL divergence between per-image distribution and marginal distribution.

• Take exponential of average KL divergence.

This gives a numerical score where higher = better.

Let’s implement this using NumPy next.

How to implement the inception score?

You can implement the Inception Score by passing generated images through a pretrained classifier (like InceptionV3), collecting softmax outputs, and computing the KL divergence between conditional and marginal class distributions.

Here’s a simplified end-to-end version using Keras and NumPy:

from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np
from scipy.stats import entropy
import tensorflow as tf
from PIL import Image

# Load pretrained InceptionV3 model
model = InceptionV3(include_top=True, weights='imagenet', pooling='avg')

def preprocess_images(img_list):
processed = []
for img in img_list:
img = img.resize((299, 299)).convert('RGB')
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
processed.append(x)
return np.vstack(processed)

def calculate_inception_score(img_list, splits=10):
imgs = preprocess_images(img_list)
preds = model.predict(imgs, verbose=0)

N = preds.shape[0]
split_scores = []

for k in range(splits):
part = preds[k * N // splits: (k+1) * N // splits]
py = np.mean(part, axis=0)
scores = [entropy(pyx, py) for pyx in part]
split_scores.append(np.exp(np.mean(scores)))

return np.mean(split_scores), np.std(split_scores)

In the above code we are using the following key points:

• InceptionV3 model is used as the feature extractor.

• Softmax predictions (class probabilities) are collected.

• KL divergence compares individual image class distribution to overall mean distribution.

• Exponential of mean KL gives the final score.

How to Calculate the Inception Score?

To compute IS:

• Use a pretrained classifier (e.g., Inception v3).

• Predict probabilities for each image.

• Compute KL divergence between per-image distribution and marginal distribution.

• Take exponential of average KL divergence.

This gives a numerical score where higher = better.

Let’s implement this using NumPy next.

How to Implement the Inception Score With NumPy?

Here is the code snippet showing the implementation of inception scores:

import numpy as np
from scipy.stats import entropy

def inception_score(preds, splits=10):
N = preds.shape[0]
split_scores = []

for k in range(splits):
part = preds[k * N // splits: (k+1) * N // splits]
py = np.mean(part, axis=0)
scores = [entropy(pyx, py) for pyx in part]
split_scores.append(np.exp(np.mean(scores)))

return np.mean(split_scores), np.std(split_scores)

preds should be a NumPy array of predicted class probabilities for each image.

Next up, let’s use Keras to automate prediction and get IS-ready scores.

How to Implement the Inception Score With Keras?

Here is the code snippet showing how to implementt inception scores withKerass:

from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np

model = InceptionV3(include_top=True, weights='imagenet', pooling='avg')

def get_predictions(img_list):
processed_imgs = np.array([preprocess_input(image.img_to_array(img.resize((299, 299)))) for img in img_list])
preds = model.predict(processed_imgs)
return preds

Combine this with the NumPy inception_score() function above to compute IS for your GAN outputs.

As you implement this, be aware of some core issues still lingering with IS.

Problems With the Inception Score

• No ground truth comparison leads to inflated scores for mode-collapsed models.

• Not suitable for all image domains due to reliance on ImageNet classes.

• No insight into visual quality like sharpness, color balance, or realism outside classification.

Despite these, IS is still widely used. Let’s wrap this up.

Conclusion

Hence, the Inception Score remains a quick and easy way to evaluate how realistic and diverse your generative model outputs are—especially when used alongside other metrics like FID. While not flawless, IS is a powerful first-step tool in the validation pipeline for Generative AI models.

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FAQs

1. What is a good Inception Score?

A good Inception Score is typically:

• > 7 for models generating realistic and diverse images (like CIFAR-10).

• Higher scores mean better quality and diversity, but ideal values depend on the dataset.

2. What is the Inception Score scale?

The Inception Score scale is unbounded but generally falls between:

• 0 to 10+

• Higher is better, indicating images are both:

  • High quality (confident classification)

  • Diverse (spread across multiple classes)

3. How to calculate the Inception Score?

Here’s a simplified version in Python:

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import torch
import torch.nn.functional as F
from torchvision.models import inception_v3
from torchvision.transforms import Resize, ToTensor, Normalize, Compose
from scipy.stats import entropy
import numpy as np

def calculate_inception_score(images, splits=10):
model = inception_v3(pretrained=True, transform_input=False).eval()
preprocess = Compose([Resize((299, 299)), ToTensor(), Normalize((0.5,), (0.5,))])

preds = []
for img in images:
img_tensor = preprocess(img).unsqueeze(0)
with torch.no_grad():
pred = F.softmax(model(img_tensor), dim=1).cpu().numpy()
preds.append(pred)

preds = np.vstack(preds)
split_scores = []

for k in range(splits):
part = preds[k * len(preds) // splits: (k+1) * len(preds) // splits]
py = np.mean(part, axis=0)
scores = [entropy(pyx, py) for pyx in part]
split_scores.append(np.exp(np.mean(scores)))

return np.mean(split_scores), np.std(split_scores)

4. What is the Inception Score in generative AI?

The Inception Score is a metric used in Generative AI (especially for GANs) to evaluate:

• Image quality (sharpness/confidence of class predictions)

• Diversity (spread across many classes)

It uses a pretrained Inception v3 model to measure how realistic and varied generated images are.