fix how-to links

docs/how_to/detect_and_annotate.md

@@ -126,15 +126,15 @@ Now that we have predictions from a model, we can load them into Supervision.
 
 You can load predictions from other computer vision frameworks and libraries using:
 
-- [`from_deepsparse`](detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
-- [`from_detectron2`](detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
-- [`from_mmdetection`](detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
-- [`from_sam`](detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
-- [`from_yolo_nas`](detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
+- [`from_deepsparse`](/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
+- [`from_detectron2`](/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
+- [`from_mmdetection`](/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
+- [`from_sam`](/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
+- [`from_yolo_nas`](/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
 
 ## Annotate Image with Detections
 
-Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes.
+Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes.
 
 === "Inference"
 
@@ -214,7 +214,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
 
 ## Display Custom Labels
 
-By default, [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator)
+By default, [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator)
 will label each detection with its `class_name` (if possible) or `class_id`. You can
 override this behavior by passing a list of custom `labels` to the `annotate` method.
 
@@ -315,9 +315,9 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
 ## Annotate Image with Segmentations
 
 If you are running the segmentation model
-[`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator)
+[`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator)
 is a drop-in replacement for
-[`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator)
+[`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator)
 that will allow you to draw masks instead of boxes.
 
 === "Inference"

docs/how_to/detect_small_objects.md

@@ -9,7 +9,7 @@ This guide shows how to detect small objects
 with the  [Inference](https://github.com/roboflow/inference),
 [Ultralytics](https://github.com/ultralytics/ultralytics) or
 [Transformers](https://github.com/huggingface/transformers) packages using
-[`InferenceSlicer`](detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
+[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
 
 <video controls>
     <source src="https://media.roboflow.com/supervision_detect_small_objects_example.mp4" type="video/mp4">
@@ -154,7 +154,7 @@ is less effective for ultra-high-resolution images (4K and above).
 
 ## Inference Slicer
 
-[`InferenceSlicer`](detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
+[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
 processes high-resolution images by dividing them into smaller segments, detecting
 objects within each, and aggregating the results.