Update doc

Author: Koldim2001

README.md

@@ -210,7 +210,7 @@ visualize_results(
 
 5. **Handling Duplicate Suppression Issues**: If you encounter issues with duplicate suppression from overlapping patches, consider adjusting the `nms_threshold` and `sorter_bins` parameters in `CombineDetections` or modifying the overlap and size parameters of the patches themselves. (PS: often lowering `sorter_bins` to 5 or 4 can help).
 
-6. **Handling Multi-Class Detection Issues**: If you are working on a multi-class detection or instance segmentation task, it may be beneficial to switch the mode to `class_agnostic_nms=False` in the `CombineDetections` parameters. The traditional mode, `class_agnostic_nms=True`, is effective primarily when dealing with a vast number of closely related classes in pre-trained YOLO networks (for example, when there is often confusion between classes like `car` and `truck`). If in your scenario, an object of one class can physically be inside an object of another class, you should definitely set `class_agnostic_nms=False` for such cases.
+6. **Handling Multi-Class Detection Issues**: If you are working on a multi-class detection or instance segmentation task, it may be beneficial to switch the mode to `class_agnostic_nms=False` in the `CombineDetections` parameters. The default mode, with `class_agnostic_nms` set to True, is particularly effective when handling a large number of closely related classes in pre-trained YOLO networks (for example, when there is often confusion between classes like `car` and `truck`). If in your scenario, an object of one class can physically be inside an object of another class, you should definitely set `class_agnostic_nms=False` for such cases.
 
 7. **High-Quality Instance Segmentation**: For tasks requiring high-quality results in instance segmentation, detailed guidance is provided in the next section of the README.
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