yolov11 detect

### YOLOv11 Object Detection Implementation and Usage

For implementing object detection using the modified version of YOLOv11 that incorporates SAConv (Switchable Atrous Convolution), one can follow a structured approach to set up, train, and utilize this advanced model. The integration of SAConv into YOLov11 aims at enhancing feature extraction capabilities by dynamically adjusting atrous rates during training[^2].

The setup process involves navigating to the directory where the YOLOv11 project is located:

cd yolov11项目所在的路径

Training configuration requires specifying parameters such as dataset path (`data`), model architecture file (`model`), number of epochs (`epochs`), image size (`imgsz`), batch size (`batch`), device type (`device`), and output location (`project`). An example command line for initiating training looks like this:

yolo detect train \
    data=coco128.yaml \
    model=ultralytics/cfg/models/11/yolo11n_SAconv.yaml \
    epochs=100 \
    imgsz=640 \
    batch=16 \
    device=cpu \
    project=yolov11

This script sets up an environment configured specifically for training on COCO-128 dataset with images resized to 640x640 pixels over 100 epochs while utilizing CPU resources.

To further enhance performance through recursive feature pyramids or switchable atrous convolutions similar concepts found in DetectoRS framework could be explored but direct application would require adapting these techniques within YOLO's architecture which might involve deeper modifications beyond simple parameter changes[^1].