As Large Language Models (LLMs) evolve, they are no longer confined to text. The rise of Vision-Language Models (VLMs) like GPT-4V, LLaVA, and Gemini Pro has unlocked new capabilities, allowing AI to interpret images, charts, and screenshots. However, evaluating these models is significantly more complex than standard text generation. You can't simply use BLEU scores or basic string matching. You need robust, custom observability tools to ensure your multi-modal applications are reliable and accurate.
In this guide, we will explore how Braintrust, a leading AI observability and evaluation platform, can be leveraged to evaluate VLMs using custom metrics. We will focus on creating specific evaluation criteria for vision tasks, such as object detection accuracy and caption quality, ensuring your deployment is grounded in real-world performance data.
Why Standard Metrics Fail for Vision-Language Models
Traditional NLP metrics like Perplexity or ROUGE are designed for text-to-text tasks. When you introduce visual data, the input space becomes vast and ambiguous. For example, a model might correctly identify an object but fail to describe its spatial relationship to another object. A simple "correct" or "incorrect" boolean is insufficient.
Braintrust allows developers to define custom metrics that align with specific business logic. This is crucial for VLMs because the "correctness" of an output often depends on the context of the image. By using Braintrust, you can log both the image input and the model's text output, then apply custom scoring functions to evaluate nuances that standard benchmarks miss.
Setting Up Custom Metrics in Braintrust
Braintrust's API makes it straightforward to define custom evaluators. You can write Python functions that take the input image, the expected ground truth (if available), and the model's output, and return a structured score. Let's look at a practical example: evaluating a VLM's ability to count objects in an image.
import braintrust
from PIL import Image
import re
def count_accuracy(input, output, ground_truth):
"""
Custom metric to evaluate if the model correctly counted objects.
Args:
input: The original image object.
output: The text response from the VLM.
ground_truth: The known number of objects (e.g., 5).
Returns:
A dict with 'score' and 'reasoning'.
"""
# Extract the first integer found in the model's output
match = re.search(r'\d+', output)
if not match:
return {
'score': 0,
'reasoning': 'No number found in output.'
}
predicted_count = int(match.group())
if predicted_count == ground_truth:
return {
'score': 1,
'reasoning': f'Correctly identified {predicted_count} objects.'
}
else:
# Partial credit if close? Or just 0 for strictness?
# Here we use strict 0/1, but you could use 1 - abs(diff)/max
return {
'score': 0,
'reasoning': f'Model predicted {predicted_count}, expected {ground_truth}.'
}
# Register the custom metric
braintrust.log(
'VLM_Count_Test',
metrics={
'count_accuracy': count_accuracy
}
)
Implementing the Evaluation Workflow
Once your custom metric is defined, you can run an evaluation across a dataset of images. Braintrust automatically tracks the cost, latency, and token usage of each request, giving you a holistic view of your VLM's performance. Here is how you might structure the evaluation run:
import base64
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
# Sample dataset structure
dataset = [
{
"input": {
"image": "path/to/dogs.jpg",
"prompt": "How many dogs are in this image?"
},
"expected": 3
},
{
"input": {
"image": "path/to/cats.png",
"prompt": "How many cats are in this image?"
},
"expected": 1
}
]
def model_call(input):
# Assuming you have an OpenAI-compatible VLM endpoint
image_b64 = encode_image(input['image'])
response = openai.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": input['prompt']},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}},
],
}
],
)
return response.choices[0].message.content
# Run the evaluation
braintrust.run(
"VLM_Count_Eval",
tasks=dataset,
model=model_call,
metrics={
'count_accuracy': count_accuracy,
'latency': braintrust.latency,
'cost': braintrust.cost
}
)
Visualizing Results and Iterating
After running the evaluation, the Braintrust dashboard will display your custom count_accuracy metric alongside cost and latency. You can filter results to see which types of images the model struggles with. Perhaps it performs well on clear studio shots but fails on noisy, real-world street views. This granular insight is invaluable for improving your prompt engineering or fine-tuning your model.
Conclusion
Evaluating Vision-Language Models requires moving beyond generic NLP metrics. By leveraging Braintrust's flexible custom metric system, developers can create evaluation pipelines that are specific to their use case. Whether you are counting objects, extracting text from screenshots, or interpreting medical imagery, custom metrics provide the precision needed to trust your AI in production. Start by defining your success criteria, code your custom evaluator, and let the data guide your optimization.