---
license: agpl-3.0
library: ultralytics
tags:
  - object-detection
  - pytorch
  - coco
  - p2-layer
  - yolov8
---

# YOLOv8-p2 COCO Pretrained Model

This model is a YOLOv8-p2 model trained on the COCO dataset, **with P2-P5 output layers.** 

## Example Usage

```angular2html
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from PIL import Image

model_path = hf_hub_download("soyeollee/yolov8x-p2-coco", "model.pt")
model = YOLO(model_path)

image_path = "/path/to/image"
output = model(Image.open(image_path))
```

## Performance

| metric (maxDets=100)                | yolov8x <br/>(official) | yolov8x-p2<br/>(this repo) | [yolov8x-p26](https://huggingface.co/soyeollee/yolov8x-p26-coco) | yolov11x <br/>(official) | [yolo11x-p2](https://huggingface.co/soyeollee/yolo11x-p2-coco) |
|-------------------------------------|--------------------------|----------------------------|------------------------------------------------------------------|--------------------------|----------------------------------------------------------------|
| AP @[ IoU=0.50:0.95 / area=   all ] | **0.540**                | **0.541**                  | **0.544**                                                        | **0.546**                | **0.553**                                                      |
| AP @[ IoU=0.50      / area=   all ] | 0.710                    | 0.712                      | 0.713                                                            | 0.716                    | 0.722                                                          |
| AP @[ IoU=0.75      / area=   all ] | 0.588                    | 0.590                      | 0.593                                                            | 0.595                    | 0.606                                                          |
| AP @[ IoU=0.50:0.95 / area= small ] | 0.360                    | 0.386                      | 0.381                                                            | 0.377                    | 0.386                                                          |
| AP @[ IoU=0.50:0.95 / area=medium ] | 0.594                    | 0.586                      | 0.586                                                            | 0.597                    | 0.595                                                          |
| AP @[ IoU=0.50:0.95 / area= large ] | 0.707                    | 0.686                      | 0.691                                                            | 0.702                    | 0.702                                                          |