init repo

README.md

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----
-license: agpl-3.0
----
+---
+license: agpl-3.0
+library: ultralytics
+tags:
+- object-detection
+- pytorch
+- coco
+- p2-layer
+- yolo11
+- yolov11
+- yolox11-p2
+- yolovx11-p2
+---
+
+# YOLO11-p2 COCO Pretrained Model
+
+This model is a YOLO11-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/yolo11x-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](https://huggingface.co/soyeollee/yolov8x-p2-coco) | [yolov8x-p26](https://huggingface.co/soyeollee/yolov8x-p26-coco) | yolov11x <br/>(official) | yolo11x-p2<br/>(this repo) |
+|--------------------------------------------------------|--------------------|----------------------------|------------------------------------------------------------------|---------------------|----------------------------|
+| 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                      |

events.out.tfevents.1733563914.ultralytics-ms.57.0

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+size 1020946

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:093dd42ff8c32843030ecfca8c390f22bf023c503247e6046dbd8da3d801b426
+size 116568922

train.py

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+from ultralytics import YOLO
+
+
+config = {
+    # project
+    'name': 'v11x-p2_coco_pretrained',
+    'model': 'yolo11x-p2.yaml',
+    'pretrained': None,
+
+    # hyperparams
+    'lr0': 0.01,
+    'lrf': 0.01,
+    'momentum': 0.937,
+    'weight_decay': 0.0005,
+    'epochs': 600,
+    'imgsz': 640,
+    'batch': 48,
+    'cos_lr': False,
+    'close_mosaic': 10,
+    'warmup_epochs': 3.0,
+    'warmup_momentum': 0.8,
+    'warmup_bias_lr': 0.0,
+    'box': 7.5,
+    'cls': 0.5,
+    'dfl': 1.5,
+    'hsv_h': 0.015,
+    'hsv_s': 0.7,
+    'hsv_v': 0.4,
+    'translate': 0.1,
+    'scale': 0.9,  # coco=0.9
+    'fliplr': 0.5,
+    'optimizer': 'SGD',
+    'patience': 100,
+    # augment: https://github.com/ultralytics/ultralytics/issues/16607
+    'mixup': 0.15,
+    'mosaic': 1.0,
+    'copy_paste': 0.3,
+
+    # system
+    'seed': 0,
+    'deterministic': True,
+    'data': 'ultralytics/cfg/datasets/coco.yaml',
+    'cache': 'disk',
+    'device': [0, 1, 2, 3],
+    'workers': 16,
+    'project': 'runs/train',
+    'val': True,
+}
+
+model = YOLO(config['model'])
+model.train(**config)

yolo11-p2.yaml

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLO11 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+# from: https://github.com/ultralytics/ultralytics/pull/16558/files
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024]
+  s: [0.50, 0.50, 1024]
+  m: [0.50, 1.00, 512]
+  l: [1.00, 1.00, 512]
+  x: [1.00, 1.50, 512]
+
+# YOLO11-P2 backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11-P2 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+  - [-1, 2, C3k2, [128, False]] # 19 (P2/4-xsmall)
+
+  - [-1, 1, Conv, [128, 3, 2]]
+  - [[-1, 16], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [256, False]] # 22 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 25 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 28 (P5/32-large)
+
+  - [[19, 22, 25, 28], 1, Detect, [nc]] # Detect(P3, P4, P5)