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Image Editing

The Image Editing metric assesses the performance of image editing tasks by evaluating the quality of synthesized images based on semantic consistency and perceptual quality (similar to the TextToImageMetric). deepeval's Image Editing metric is a self-explaining MLLM-Eval, meaning it outputs a reason for its metric score.

Required Arguments​

To use the ImageEditingMetric, you'll have to provide the following arguments when creating an MLLMTestCase:

  • input
  • actual_output
note

Both the input and output should each contain exactly 1 image.

Example​

from deepeval import evaluate
from deepeval.metrics import ImageEditingMetric
from deepeval.test_case import MLLMTestCase, MLLMImage

# Replace this with your actual MLLM application output
actual_output=[MLLMImage(url="https://shoe-images.com/edited-shoes", local=False)]

metric = ImageEditingMetric(
    threshold=0.7,
    include_reason=True,
)
test_case = MLLMTestCase(
    input=["Change the color of the shoes to blue.", MLLMImage(url="./shoes.png", local=True)],
    actual_output=actual_output,
    retrieval_context=retrieval_context
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)

# or evaluate test cases in bulk
evaluate([test_case], [metric])

There are five optional parameters when creating a ImageEditingMetric:

  • [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
  • [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
  • [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
  • [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
  • [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

How Is It Calculated?​

The ImageEditingMetric score is calculated according to the following equation:

O=min(α1,…,αi)⋅min(β1,…,βi)O = \sqrt{\text{min}(\alpha_1, \ldots, \alpha_i) \cdot \text{min}(\beta_1, \ldots, \beta_i)}

The ImageEditingMetric score combines Semantic Consistency (SC) and Perceptual Quality (PQ) sub-scores to provide a comprehensive evaluation of the synthesized image. The final overall score is derived by taking the square root of the product of the minimum SC and PQ scores.

SC Scores​

These scores assess aspects such as alignment with the prompt and resemblance to concepts. The minimum value among these sub-scores represents the SC score. During the SC evaluation, both the input conditions and the synthesized image are used.

PQ Scores​

These scores evaluate the naturalness and absence of artifacts in the image. The minimum value among these sub-scores represents the PQ score. For the PQ evaluation, only the synthesized image is used to prevent confusion from the input conditions.