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Update README.md with additional information

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@@ -48,16 +48,16 @@ The model was trained on a filtered version of the COCO dataset containing only
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  The model achieves the following metrics on the COCO person validation set:
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- | Metric | Value |
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- | ------------------------- | ---------- |
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- | Box mAP50-95 (COCO) | 0.628 |
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- | Box mAP50 (COCO) | 0.840 |
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- | Mask mAP50-95 | 0.524 |
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- | Mask mAP50 | 0.821 |
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- | Box Precision | 0.835 |
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- | Box Recall | 0.745 |
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- | Mask Precision | 0.843 |
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- | Mask Recall | 0.723 |
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  These metrics were computed on a validation set of 5,000 images with 10,777 instances.
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@@ -73,7 +73,7 @@ This model is ideal for applications requiring precise person segmentation:
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  ## Usage
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- The model can be used directly with Ultralytics YOLOv8:
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  ```python
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  from ultralytics import YOLO
@@ -136,4 +136,4 @@ if result.masks is not None:
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  ## License
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- This model is available under the Apache 2.0 license.
 
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  The model achieves the following metrics on the COCO person validation set:
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+ | Metric | Value |
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+ | ------------------- | ----- |
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+ | Box mAP50-95 (COCO) | 0.628 |
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+ | Box mAP50 (COCO) | 0.840 |
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+ | Mask mAP50-95 | 0.524 |
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+ | Mask mAP50 | 0.821 |
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+ | Box Precision | 0.835 |
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+ | Box Recall | 0.745 |
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+ | Mask Precision | 0.843 |
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+ | Mask Recall | 0.723 |
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  These metrics were computed on a validation set of 5,000 images with 10,777 instances.
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  ## Usage
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+ The model can be used directly with Ultralytics YOLO:
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  ```python
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  from ultralytics import YOLO
 
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  ## License
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+ This model is available under the Apache 2.0 license.