Custom YOLOv3 pytorch for PPE detection.

Check this youtube link for video sample result Youtube link

Training the custom PPE dataset on YOLOv3

Steps

We have added a small dataset for PPE detection dataset in the folder called customdataset. Download the full dataset from Google drive This downloadable dataset will have 3000+ images and labels labeled using annotation tool given in the repo

Full credit goes to this, and if you are looking for much more detailed explainiation and features, please refer to the original source.

You'll need to download the weights from the original source.

1. Create a folder called weights in the root (YoloV3) folder
2. Download from: https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0
3. Place 'yolov3-spp-ultralytics.pt' file in the weights folder:

• to save time, move the file from the above link to your GDrive
• then drag and drop from your GDrive opened in Colab to weights folder

4. run this command python train.py --data data/smalcoco/smalcoco.data --batch 10 --cache --epochs 25 --nosave

For custom dataset:

1. use the annotation tool given in the repo, for more details read here
2. Follow the installation steps as mentioned in the repo.
3. For the assignment, download 500 images of your unique object.
4. Annotate the images using the Annotation tool.

data
  --customdata
    --images/
      --img001.jpg
      --img002.jpg
      --...
    --labels/
      --img001.txt
      --img002.txt
      --...
    custom.data #data file
    custom.names #your class names
    custom.txt #list of name of the images you want your network to be trained on. Currently we are using same file for test/train

5. As you can see above you need to create custom.data file. For 4 class example, your file will look like this:

  classes=4
  train=data/customdata/train.txt
  test=data/customdata/test.txt 
  names=data/customdata/custom.names

6. As you it a poor idea to keep test and train data same, but the point of this repo is to get you up and running with YoloV3 asap. You'll probably do a mistake in writing to custom.txt file. This is how our file looks like (please note the .s and /s):

./data/customdata/images/img001.jpg
./data/customdata/images/img002.jpg
./data/customdata/images/img003.jpg
...

7. You need to add custom.names file as you can see above. For our example, we downloaded images of Walle. Our custom.names file look like this:

walle

8. Walle above will have a class index of 0.
9. For COCO's 80 classes, VOLOv3's output vector has 255 dimensions ( (4+1+80)*3). Now we have 1 class, so we would need to change it's architecture.
10. Copy the contents of 'yolov3-spp.cfg' file to a new file called 'yolov3-custom.cfg' file in the data/cfg folder.
11. Search for 'filters=255' (you should get entries entries). Change 255 to 27 = (4+1+4)*3
12. Search for 'classes=80' and change all three entries to 'classes=1'
13. Since you are lazy (probably), you'll be working with very few samples. In such a case it is a good idea to change:

• burn_in to 100
• max_batches to 5000
• steps to 4000,4500

14. Don't forget to perform the weight file steps mentioned in the sectio above.
15. Run this command python train.py --data data/customdata/custom.data --batch 10 --cache --cfg cfg/yolov3-custom.cfg --epochs 3 --nosave

Results After training for 30 Epochs, results look awesome!