Clean up

Author: tyler-romero

docs/docs/answer-modes/1-binary-detectors.md

@@ -0,0 +1,55 @@
+# Binary Classification Detectors
+
+Binary classification detectors are used to answer yes/no questions about images. Most of Groundlight's documentation examples are for binary classification detectors, as they are the simplest type of detector.
+
+In order to create a binary classification detector, you need to provide a query that asks a yes/no question. For example, "Is there an eagle visible?" or "Is the door fully closed?".
+
+```python
+from groundlight import Groundlight
+gl = Groundlight()
+
+# highlight-start
+detector = gl.create_detector(
+    name="eagle-detector",
+    query="Is there an eagle visible?",
+    confidence_threshold=0.9,
+)
+# highlight-end
+```
+
+## Submit an Image Query to a Binary Classification Detector
+
+Now that you have created a binary classification detector, you can submit an image query to it.
+
+```python notest
+from groundlight import Groundlight
+gl = Groundlight()
+
+detector = gl.get_detector_by_name("eagle-detector")
+
+# highlight-start
+# Check if an eagle is visible in an image
+image_query = gl.submit_image_query(detector, "path/to/image.jpg")
+# highlight-end
+
+print(f"Result: {image_query.result.label}")
+print(f"Confidence: {image_query.result.confidence}")
+```
+
+Binary classification detectors return a `label` attribute in the result object, which will be either `"YES"` or `"NO"`. If a query is ambiguous, it is also possible for the detector to return an `"UNCLEAR"` label.
+
+The `confidence` attribute represents the confidence level in the predicted label, which (for a binary classification detector) is a value between 0.5 and 1. A higher confidence score indicates that the model is more certain about its prediction.
+
+## Add a label to a Binary Classification Detector
+
+To provide ground truth labels for binary classification detectors, you can specify the label as either `"YES"`, `"NO"`, or `"UNCLEAR"`. This helps improve the accuracy of your detector over time.
+
+```python notest
+from groundlight import Groundlight
+gl = Groundlight()
+
+# highlight-start
+# Add a binary label to the image query from the previous example
+gl.add_label(image_query, label="YES")
+# highlight-end
+```

docs/docs/answer-modes/2-multi-class-detectors.md docs/docs/answer-modes/2-multi-choice-detectors.md

@@ -1,4 +1,4 @@
-# [BETA] Multi-Class Detectors
+# Multiple Choice (Choose One) Detectors
 
 If you want to classify images into multiple categories, you can create a multi-class detector.
 
@@ -20,9 +20,9 @@ detector = gl_exp.create_multiclass_detector(
 We recommend adding an "Other" class to your multi-class detector to handle cases where the image does not belong to any of the pre-defined classes.
 :::
 
-<!-- :::note
+:::note
 Multi-Class Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing).
-::: -->
+:::
 
 ## Submit an Image Query to a Multi-Class Detector
 

docs/docs/answer-modes/3-counting-detectors.md

@@ -1,4 +1,4 @@
-# Counting Detectors
+# Count Detectors
 
 Counting detectors are used to count the number of objects in an image. Groundlight's counting detectors also return bounding boxes around the objects they count.
 

docs/docs/answer-modes/answer-modes.md

@@ -2,8 +2,9 @@
 
 Groundlight offers several detector modalities to suit different computer vision tasks. While previous examples have focused on binary classification, this guide will walk you through using counting and multi-class detectors. Let's explore how these different modes can be used via the Groundlight SDK.
 
-1. [Binary Detectors](1-binary-detectors.md)
-2. [Multi-class Detectors](2-multi-class-detectors.md)
-3. [Counting Detectors](3-counting-detectors.md)
+- **[Binary Detectors](1-binary-detectors.md)**: Learn how to create detectors that answer yes/no questions about images.
+- **[Multiple Choice (Choose One) Detectors](2-multi-choice-detectors.md)**: Create detectors that select one answer from a predefined list of options.
+- **[Count Detectors](3-counting-detectors.md)**: Use detectors to count the number of objects present in an image - and return bounding boxes around the counted objects.
+<!-- 4. [Text Recognition Detectors](4-text-recognition-detectors.md) -->
 
-<!-- TODO: text, object detection modes -->
+<!-- TODO: object detection modes -->