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Writing Queries

Introduction​

With Groundlight's detectors, you can ask binary questions about images — i.e., the answer should be unambiguously "YES" or "NO". If you ask an ambiguous question, you may receive an "UNSURE" response.

detector = gl.get_or_create_detector(
    name="Conveyor belt boxes",
    query="Are there any cardboard boxes on the conveyor belt?"
)
image_query = gl.submit_image_query(detector=detector, image=some_image)

# The SDK can return "YES" or "NO" (or "UNSURE")
print(f"The answer is {image_query.result.label}")

So, what makes a good question? Let's look at a few good ✅, moderate 🟡, and bad ❌ examples!

Examples​

✅ Are there any cardboard boxes on the conveyor belt?​

This question is binary and can be answered unambiguously with a simple "YES" or "NO" based on the image content.

🟡 Is the trash can full?​

This question is okay, but it could be rephrased to be more specific. For example, "Is the black trash can more than 80% full?"

tip

With Groundlight, your questions may be routed to a machine learning model or a human reviewer. One way to improve your questions is to think, "If I saw this question for the first time, would I know precisely what the person was trying to convey?"

✅ Is the garage door completely closed?​

The query is very specific about what "YES" means. According to this query, any slight / partial opening would be considered "NO".

🟡 Is the weather nice out?​

This question is somewhat ambiguous. Different people may have different opinions on what is nice weather. Instead, you might ask "Can you see any clouds in the sky?"

❌ Where is the thing?​

This is not a binary question — "YES" and "NO" don't make sense in this context. Also, it's not clear what the "thing" refers to.

🟡 Is the factory floor clean and organized?​

While this question is binary, "cleanliness" can be somewhat subjective. An improved version could be: "Are there any visible spills or clutter on the factory floor?"