open-lpr pretrained models

Pre trained models consisting of data from coco, open-lpr-dataset-plate-detection and other datasets.

This repository is part of the OpenLPR project.

There are two models, a full precision fp32 model and a mixed prcision model. Both yield similar results. These models have been generated using the Open LPR Dataset

The training was conducated using images from SSD Pytorch repository

To download the models, use the following urls

Model	Download link	Command to download
fp32	https://1drv.ms/u/s!AvNYo0I9EXbwh1hpdp-r-1qENn_t?e=nKbmnB	wget -O ssd.pth 'https://api.onedrive.com/v1.0/shares/s!AvNYo0I9EXbwh1hpdp-r-1qENn_t/root/content'
amp	https://1drv.ms/u/s!AvNYo0I9EXbwh1mFaEqZNmUjQs99	wget -O ssd.pth 'https://api.onedrive.com/v1.0/shares/s!AvNYo0I9EXbwh1mFaEqZNmUjQs99/root/content'

Training instructions

The following contents will assume that we are working in $HOME/work/repos which will be the root folder containing everything related to open lpr.

IMPORTANT: If you have a different folder structure please review the configuration files for each tool/repo for adjustments.

Overall the process to train your custom model consists of the following steps

• Downloading the coco dataset
• Checking out the open lpr plate detection dataset
• Preparing open lpr training dataset
• Cloning the SSD repository
• Building the training container and initiating the training process

Downloading the coco dataset

Execute the following commands to checkout the coco dataset

mkdir datasets && cd datasets && \
wget -O download_coco.sh 'https://raw.githubusercontent.com/faisalthaheem/open-lpr-pretrained-models/main/download_coco.sh' && \
chmod +x download_coco.sh && sh download_coco.sh

Checking out the open lpr plate detection dataset

While being in the datasets directory

git clone https://github.com/faisalthaheem/open-lpr-dataset-plate-detection

Preparing open lpr training dataset

While being in the datasets directory

mkdir cars_from_coco

We begin with extracting the train and val images from coco dataset containing vehicles

docker run --rm -it -u $UID \
-v $PWD:/datasets \
faisalthaheem/simanno-scripts:main \
"/usr/local/bin/python3.8 import-cat-from-coco.py -t val -c car -li 1 -af /datasets/coco/annotations/instances_val2017.json -dp /datasets/cars_from_coco -sp /datasets/coco/val2017/"

docker run --rm -it -u $UID \
-v $PWD:/datasets \
faisalthaheem/simanno-scripts:main \
"/usr/local/bin/python3.8 import-cat-from-coco.py -t train -c car -li 1 -af /datasets/coco/annotations/instances_train2017.json -dp /datasets/cars_from_coco -sp /datasets/coco/train2017/"

Finally we merge the extracted coco dataset with the open lpr plate detection dataset to get the training dataset

cat <<EOT > mergedbs.yaml
dest:
  # Paths to the destination databases and directories
  train_db: /datasets/openlpr/train.db
  val_db: /datasets/openlpr/val.db
  train_path: /datasets/openlpr/train
  val_path: /datasets/openlpr/val
  # label_mapping defines how labels ids from source databases map into
  # the destination database
  label_mapping:
    coco_cars:
      1: 1
    plate_det:
      1: 2
src:
  - coco_cars:
      train_db: /datasets/cars_from_coco/train.db
      val_db: /datasets/cars_from_coco/val.db
      train_path: /datasets/cars_from_coco/train
      val_path: /datasets/cars_from_coco/val
  - plate_det:
      train_db: /datasets/open-lpr-dataset-plate-detection/train.db
      val_db: /datasets/open-lpr-dataset-plate-detection/val.db
      train_path: /datasets/open-lpr-dataset-plate-detection/train
      val_path: /datasets/open-lpr-dataset-plate-detection/val
EOT

docker run --rm -it -u $UID \
-v $PWD:/datasets \
faisalthaheem/simanno-scripts:main "/usr/local/bin/python3.8 /simanno/scripts/mergedbs.py -c /datasets/mergedbs.yaml"

Cloning the SSD repository

Should be executed in $HOME/work/repos

git clone https://github.com/faisalthaheem/SSD-pytorch

Building the training container and initiating the training process

The following command will build an image which shold allow training the models using mixed precision

cd SSD-pytorch && \
sh build-docker-image.sh

Once the image is built we can launch a container with the following command

export DS_DIR=$HOME/work/repos/datasets
export ROOT_CACHE_DIR=$HOME/work/repos/mountedvols/root-cache
export CHECKPOINT_DIR=$HOME/work/repos/checkpoints
sh run-training-container.sh

Once the container is running, the following command can be used to begin the training process using mixed precision

python3 train.py --save-folder /checkpoints/openlpr-amp --batch-size 42 --epochs 30 --amp --dist --num-workers 16

Please note you may need to adjust the parameters in the preceding command given the amount of resources available, the above command was tailored for a system running stock ubuntu desktop 20.04 with an RTX 2070 (8GB).

Once the training is completed you should be able to see an output similar to following

Epoch: 1. Loss: 5.09479: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348/348 [03:12<00:00,  1.80it/s]
Epoch: 2. Loss: 4.56174: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348/348 [03:09<00:00,  1.84it/s]
Epoch: 3. Loss: 3.95576: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348/348 [03:02<00:00,  1.90it/s]
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Epoch: 29. Loss: 3.32882:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                 | 263/348 [02:17<00:44,  1.92it/s]Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 65536.0
Epoch: 29. Loss: 3.03714: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348/348 [03:01<00:00,  1.92it/s]
Epoch: 30. Loss: 2.46069: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348/348 [03:01<00:00,  1.92it/s]

At this point, the model should be available on the host machine under checkpoints/openlpr-amp directory and can be used with openalpr project.