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Neural Architecture Search：使用Ultralytics框架进行YOLO-NAS目标检测

前言
相关介绍
前提条件
实验环境
安装环境
项目地址
- Linux
- Windows
使用Ultralytics框架进行YOLO-NAS目标检测
- 进行预测
- 进行验证
参考文献

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前言

前提条件

实验环境

matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.6.0
Pillow>=7.1.2
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.64.0
tensorboard>=2.4.1
pandas>=1.1.4
seaborn>=0.11.0

安装环境

pip install ultralytics
# 或者
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple # 国内清华源，下载速度更快

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项目地址

Linux

git clone https://github.com/ultralytics/ultralytics.git

Cloning into 'ultralytics'...
remote: Enumerating objects: 4583, done.
remote: Counting objects: 100% (4583/4583), done.
remote: Compressing objects: 100% (1270/1270), done.
remote: Total 4583 (delta 2981), reused 4576 (delta 2979), pack-reused 0
Receiving objects: 100% (4583/4583), 23.95 MiB | 1.55 MiB/s, done.
Resolving deltas: 100% (2981/2981), done.

Windows

使用Ultralytics框架进行YOLO-NAS目标检测

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进行预测

python predict.py

from ultralytics import NAS


if __name__=="__main__":
    # Load a COCO-pretrained YOLO-NAS-s model
    model = NAS("yolo_nas_s.pt")

    # Display model information (optional)
    model.info()

    # Run inference with the YOLO-NAS-s model on the image
    results = model("imgs/test.png", save=True)

    results[0].show()

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进行验证

python val.py

from ultralytics import YOLO

def val():

    # Create a YOLO-World model
    model = YOLO("yolov8s-world.pt")  # or select yolov8m/l-world.pt for different sizes

    # Conduct model validation on the COCO8 example dataset
    metrics = model.val(data="coco8.yaml", batch=1, imgsz=640, device=0,workers=1)

if __name__=="__main__":
    val()

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参考文献

[1] Ultralytics 源代码地址：https://github.com/ultralytics/ultralytics.git.
[2] Ultralytics Docs：https://docs.ultralytics.com/
[3] SAM2 官方源代码：https://github.com/facebookresearch/segment-anything-2.git
[4] SAM2 论文地址：https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/
[5] https://docs.ultralytics.com/models/sam/
[6] https://docs.ultralytics.com/models/sam-2/
[7] https://segment-anything.com/
[8] https://arxiv.org/abs/2304.02643
[9] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick. Segment Anything. 2023
[10] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chay Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun,Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick,Piotr Dollar, Christoph Feichtenhofer. SAM 2: Segment Anything in Images and Videos. 2024