YOLOv7 Instance Segmentation

Steps to run Code

• Clone the repository

git clone https://github.com/RizwanMunawar/yolov7-segmentation.git

• Go to the cloned folder.

cd yolov7-segmentation

• Create a virtual environment (Recommended, if you don't want to disturb Python packages)

### For Linux Users
python3 -m venv yolov7seg
source yolov7seg/bin/activate

### For Windows Users
python3 -m venv yolov7seg
cd yolov7seg
cd Scripts
activate
cd ..
cd ..

• Upgrade pip with the mentioned command below.

pip install --upgrade pip

• Install requirements with the mentioned command below.

pip install -r requirements.txt

• Download weights from link and store in "yolov7-segmentation" directory.

• Run the code with the mentioned command below.

#for segmentation with detection
python3 segment/predict.py --weights yolov7-seg.pt --source "videopath.mp4"

#for segmentation with detection + Tracking
python3 segment/predict.py --weights yolov7-seg.pt --source "videopath.mp4" --trk

#save the labels files of segmentation
python3 segment/predict.py --weights yolov7-seg.pt --source "videopath.mp4" --save-txt

• Output file will be created in the working directory with name runs/predict-seg/exp/original-video-name.mp4

RESULTS

Car Semantic Segmentation	Car Semantic Segmentation	Person Segmentation + Tracking

Custom Data Labelling

• I have used roboflow for data labelling. The data labelling for Segmentation will be a Polygon box, while data labelling for object detection will be a bounding box

• Go to the link and create a new workspace. Make sure to log in with your Roboflow account.

• Once you click on create workspace, you will see the pop-up as shown below to upload the dataset.

• Click on upload dataset, and Roboflow will ask for the workspace name as shown below. Fill that form and then click on Create Private Project
• Note: Make sure to select Instance Segmentation Option in below image.

-You can upload your dataset now.

• Once the files are uploaded, you can click on Finish Uploading.

• Roboflow will ask you to assign Images to someone, click on Assign Images.

• After that, you will see the tab shown below.

• Click on any Image in Unannotated tab, and then you can start labelling.

• Note: Press p and then draw polygon points for segmentation

• Once you have completed labelling, you can then export the data and follow the steps mentioned below to start training.

Custom Training

• Move your (segmentation custom labelled data) inside the yolov7-segmentation/data folder by following the mentioned structure.

• Go to the data folder, create a file with name custom.yaml and paste the mentioned code below inside that.

train: "path to train folder"
val: "path to validation folder"
# number of classes
nc: 1
# class names
names: [ 'car']

• Download weights from the link and move to yolov7-segmentation folder.
• Go to the terminal, and run the mentioned command below to start training.

python3 segment/train.py --data data/custom.yaml \
                          --batch 4 \
                          --weights "yolov7-seg.pt"
                          --cfg yolov7-seg.yaml \
                          --epochs 10 \
                          --name yolov7-seg \
                          --img 640 \
                          --hyp hyp.scratch-high.yaml

Custom Model Detection Command

python3 segment/predict.py --weights "runs/yolov7-seg/exp/weights/best.pt" --source "videopath.mp4"

RESULTS

Car Semantic Segmentation	Car Semantic Segmentation	Person Segmentation + Tracking

References

• https://github.com/WongKinYiu/yolov7/tree/u7/seg
• https://github.com/ultralytics/yolov5

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More Information

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