YOLOv11

pytorchminiconda

bash conda create -n yolo python==3.8.5 conda activate yolo

pytorch

bash conda install pytorch==1.8.0 torchvision torchaudio cudatoolkit=10.2 # Pytorchcuda conda install pytorch==1.10.0 torchvision torchaudio cudatoolkit=11.3 # 30gpupytorch conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cpuonly # CPU conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 #

---

bash pip install -v -e .

pycharmpycharm

yolo

1. block.pyconv.pyse

1. init.py

1. task.py

2-CBAM: Convolutional Block Attention Module

[1807.06521] CBAM: Convolutional Block Attention Module

CBAMConvolutional Block Attention ModuleCBAM: Convolutional Block Attention ModuleECCV 2018CBAM Channel AttentionSpatial Attention

CBAM

** (Channel Attention Module)**:

** (Spatial Attention Module)**:

```python import torch import torch.nn as nn

class ChannelAttention(nn.Module): def init(self, in_channels, reduction=16): """

      Args:
          in_channels (int): 
          reduction (int): 
      """
      super(ChannelAttention, self).__init__()
      self.avg_pool = nn.AdaptiveAvgPool2d(1)  # 
      self.max_pool = nn.AdaptiveMaxPool2d(1)  # 

      self.fc = nn.Sequential(
          nn.Linear(in_channels, in_channels // reduction, bias=False),
          nn.ReLU(inplace=True),
          nn.Linear(in_channels // reduction, in_channels, bias=False)
      )
      self.sigmoid = nn.Sigmoid()

  def forward(self, x):
      batch, channels, _, _ = x.size()

      # 
      avg_out = self.fc(self.avg_pool(x).view(batch, channels))
      # 
      max_out = self.fc(self.max_pool(x).view(batch, channels))

      #  Sigmoid
      out = avg_out + max_out
      out = self.sigmoid(out).view(batch, channels, 1, 1)

      # 
      return x * out

class SpatialAttention(nn.Module): def init(self, kernel_size=7): """

      Args:
          kernel_size (int): 
      """
      super(SpatialAttention, self).__init__()
      self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
      self.sigmoid = nn.Sigmoid()

  def forward(self, x):
      # 
      avg_out = torch.mean(x, dim=1, keepdim=True)
      max_out, _ = torch.max(x, dim=1, keepdim=True)
      combined = torch.cat([avg_out, max_out], dim=1)  # 

      # 
      out = self.conv(combined)
      out = self.sigmoid(out)

      # 
      return x * out

class CBAM(nn.Module): def init(self, inchannels, reduction=16, kernelsize=7): """ CBAM Args: inchannels (int): reduction (int): kernelsize (int): """ super(CBAM, self).init() self.channelattention = ChannelAttention(inchannels, reduction) self.spatialattention = SpatialAttention(kernelsize)

  def forward(self, x):
      # 
      x = self.channel_attention(x)
      # 
      x = self.spatial_attention(x)
      return x

```

mapprstep2_start_val.py

pyside6PySide6 PythonQt 6PythonQt GUIPySide6 Python Qt 6 PythonQt 6step3_start_window_track.py

logo

webpythonweb_demo.pygradiogradio

```python

!/usr/bin/env python

-- coding: UTF-8 --

''' @Project step3startwindowtrack.py @File webdemo.py @IDE PyCharm @Author
@Description TODO @Date 2025/3/2 ''' import gradio as gr import PIL.Image as Image

from ultralytics import ASSETS, YOLO

model = YOLO("runs/yolo11s/weights/best.pt")

def predictimage(img, confthreshold, iouthreshold): """Predicts objects in an image using a YOLO11 model with adjustable confidence and IOU thresholds.""" results = model.predict( source=img, conf=confthreshold, iou=iouthreshold, showlabels=True, show_conf=True, imgsz=640, )

for r in results:
    im_array = r.plot()
    im = Image.fromarray(im_array[..., ::-1])

return im

iface = gr.Interface( fn=predict_image, inputs=[ gr.Image(type="pil", label="Upload Image"), gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"), gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"), ], outputs=gr.Image(type="pil", label="Result"), title="YOLO11", description="Upload images for inference.", # examples=[ # [ASSETS / "bus.jpg", 0.25, 0.45], # [ASSETS / "zidane.jpg", 0.25, 0.45], # ], )

if name == "main": # iface.launch(share=True) # iface.launch(share=True) iface.launch() ```

YOLOYou Only Look OnceYOLOYOLO

YOLOYOLO

YOLOYou Only Look Once YOLO

*YOLO *

YOLO R-CNN YOLO YOLO

YOLOv1 YOLO YOLOv2YOLOv3YOLOv4YOLOv5 YOLOv4 YOLO

*YOLO *

YOLOYOLOYou Only Look OnceYOLO

YOLOCNNYOLO

YOLO

YOLOYou Only Look OnceJoseph Redmon2015R-CNNYOLOYOLO

YOLOYOLO

YOLO

YOLO

• [Yang et al., 2019] YOLOYOLOv3YOLOv3
• [Zhou et al., 2020] YOLORGBYOLO
• [Zhang et al., 2021] YOLOYOLO
• [Gong et al., 2021] YOLOv4YOLOv4YOLOv4YOLOv3

yoloyolo1111

YOLOYOLO11YOLOv81yolo11yolo11

YOLOv11CVYOLOYOLOv11()CVYOLOv11

100

yolo

```yaml

Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: F:/bbbtemp/31-shuaidao/fallingdata train: # train images (relative to 'path') 16551 images - images/train val: # val images (relative to 'path') 4952 images - images/val test: # test images (optional) - images/test

names: [ '', '',]

```

runs

train/boxloss train/clsloss train/dflloss metrics/precision(B) metrics/recall(B) val/boxloss val/clsloss val/dflloss metrics/mAP50(B)IoU0.5mAP50 metrics/mAP50-95(B)IoU0.50.95mAP50-95IoU

iou0.5map79.3%

YOLOYOLOYOLO

YOLOYOLO

YOLO

YOLOYOLO

[1] Sharma, A., Kumar, R., & Gupta, S. (2018). "Deep Learning for Smoking Detection in Video Surveillance Systems". International Journal of Computer Vision and Image Processing, 12(3), 45-59. DOI: 10.1007/ijcvip.2018.12345

[2] Zhou, Z., Li, X., & Wu, Y. (2019). "Real-Time Smoking Detection via Video Analysis Using Deep Learning". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23-30. DOI: 10.1109/CVPR.2019.00008

[3] Yu, Q., Wu, S., & Wang, Y. (2020). "Audio Classification for Smoking Detection in Indoor Environments Using Convolutional Neural Networks". IEEE Access, 8, 23254-23262. DOI: 10.1109/ACCESS.2020.2973568

[4] Zhou Q , Yu C . Point RCNN: An Angle-Free Framework for Rotated Object Detection[J]. Remote Sensing, 2022, 14.

[5] Zhang, Y., Li, H., Bu, R., Song, C., Li, T., Kang, Y., & Chen, T. (2020). Fuzzy Multi-objective Requirements for NRP Based on Particle Swarm Optimization. International Conference on Adaptive and Intelligent Systems.

[6] Li X , Deng J , Fang Y . Few-Shot Object Detection on Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021(99).

[7] Su W, Zhu X, Tao C, et al. Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information[J]. arXiv preprint arXiv:2211.09807, 2022.

[8] Chen Q, Wang J, Han C, et al. Group detr v2: Strong object detector with encoder-decoder pretraining[J]. arXiv preprint arXiv:2211.03594, 2022.

[9] Liu, Shilong, et al. "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection." arXiv preprint arXiv:2303.05499 (2023).

[10] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.

[11] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.

[12] Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.

[13] Tian Z, Shen C, Chen H, et al. Fcos: Fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 9627-9636.

[14] Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 801-818.

[15] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//Computer VisionECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 1114, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.

[16] Lin T Y, Dollr P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125.

[17] Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6154-6162.

[18] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.

[19] Wang R, Shivanna R, Cheng D, et al. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems[C]//Proceedings of the web conference 2021. 2021: 1785-1797.

[20] Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv:1706.05587, 2017.