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https://github.com/zaki1003/YOLO-CROWD
YOLO-CROWD is a lightweight crowd counting and face detection model that is based on Yolov5s and can run on edge devices, as well as fixing the problems of face occlusion, varying face scales, and other challenges of crowd counting
https://github.com/zaki1003/YOLO-CROWD
Last synced: 3 months ago
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YOLO-CROWD is a lightweight crowd counting and face detection model that is based on Yolov5s and can run on edge devices, as well as fixing the problems of face occlusion, varying face scales, and other challenges of crowd counting
- Host: GitHub
- URL: https://github.com/zaki1003/YOLO-CROWD
- Owner: zaki1003
- License: mit
- Created: 2023-07-09T19:06:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-13T23:57:39.000Z (over 1 year ago)
- Last Synced: 2024-04-23T23:32:32.369Z (7 months ago)
- Language: Jupyter Notebook
- Size: 5.76 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Crowd-Counting - [YOLO-CROWD - CROWD.svg?logo=github&label=Stars) a lightweight crowd counting and face detection model that is based on [[YOLO-FaceV2](https://github.com/Krasjet-Yu/YOLO-FaceV2)]  (Misc / Code)
README
# YOLO-CROWD
YOLO-CROWD is a lightweight crowd counting and face detection model that is based on Yolov5s and can run on edge devices, as well as fixing the problems of face occlusion, varying face scales, and other challenges of crowd counting
## Description
Deep learning-based algorithms for face and crowd identification have advanced significantly. These algorithms can be broadly categorized into two groups: one-stage detectors like YOLO and two-stage detectors like Faster R-CNN. One-stage detectors have been widely employed in many applications due to the better balance between accuracy and speed, but as we are all aware, YOLO algorithms are significantly impacted by occlusion in crowd scenarios. In our project, we propose a real-time crowd counter and face detector called **YOLO-CROWD**, which has an inference speed of **10.1 ms** and contains 461 layers and 18388982 parameters. It is based on the one-stage detector YOLOv5. In order to improve the receptive field of small faces, we use a Receptive Field Enhancement module termed RFE. We then use NWD Loss to compensate for the sensitivity of IoU to the position deviation of small objects. We also employ Repulsion Loss to address face occlusion and utilize an attention module called
SEAM.
## Demo
### Images
### Videos
#### without showing label
https://github.com/zaki1003/YOLO-CROWD/assets/65148928/b0a57b00-ae72-4a5c-ad68-442be1889e0a
#### with showing label (name + conf)
https://github.com/zaki1003/YOLO-CROWD/assets/65148928/44753430-c5ef-4c15-80c7-e0f328670aac
## Comparison Between Yolov5s And YOLO-CROWD
| | [email protected] | [email protected] | Precision | Recall | Box loss | Object loss | Inference Time (ms) |
|:-------------------|:---------------|:--------------|:-------------|:-----------|:------------------|:------------------|:-----------------------------|
| Yolov5s | 39.4 | 0.15 | 0.754 | 0.382 | 0.120 | 0.266 | **7** |
| YOLO-CROWD | **43.6** | **0.158** | **0.756** | **0.424** | **0.091** | **0.158** | 10.1 |
## Environment Requirments
Create a Python Virtual Environment.
```shell
conda create -n {name} python=x.x
```
Enter Python Virtual Environment.
```shell
conda activate {name}
```
```shell
!pip install install torch==1.11 torchvision==0.12 torchtext==0.12 torchaudio==0.11
```
Install other python package.
```shell
pip install -r requirements.txt
```
## Step-Through Example
### Installation
Get the code.
```shell
git clone https://github.com/zaki1003/YOLO-CROWD.git
```
### Dataset
Download our Dataset [crowd-counting-dataset-w3o7w](https://universe.roboflow.com/crowd-dataset/crowd-counting-dataset-w3o7w), while exporting the dataset select **YOLO v5 PyTorch** Format.
## Preweight
The link is [yolov5s.pt](https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt)
### Training
Train your model on **crowd-counting-dataset-w3o7w** dataset.
```shell
python train.py --img 416
--batch 16
--epochs 200
--data {dataset.location}/data.yaml
--cfg models/yolo-crowd.yaml
--weights yolov5s.pt
--name yolo_crowd_results
--cache
```
## Postweight
The link is [yolo-crowd.pt](https://drive.google.com/file/d/1xxXVCzseuzmHv7NoMQ03RVU_tDisWXjM/view?usp=sharing)
If you want to have more inference speed try to install TensorRt and use this vesion [yolo-crowd.engine](https://drive.google.com/file/d/1-189sscpNZBFaSHOz7dnEgAaFeUALiow/view?usp=sharing)
### Test
```shell
python detect.py --weights yolo-crowd.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
```
## Results
## Finetune
see in *[https://github.com/ultralytics/yolov5/issues/607](https://github.com/ultralytics/yolov5/issues/607)*
```shell
# Single-GPU
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
# Multi-GPU
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
nohup python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --device $i --evolve > evolve_gpu_$i.log &
done
# Multi-GPU bash-while (not recommended)
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
"$(while true; do nohup python train.py... --device $i --evolve 1 > evolve_gpu_$i.log; done)" &
done
```
## Reference
*[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)*
*[https://github.com/deepcam-cn/yolov5-face](https://github.com/Krasjet-Yu/YOLO-FaceV2)*
*[https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)*
*[https://github.com/dongdonghy/repulsion_loss_pytorch](https://github.com/dongdonghy/repulsion_loss_pytorch)*
## Contact
We use code's license is MIT License. The code can be used for business inquiries or professional support requests.