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PaddleDetection (object detection)

PaddleDetection is an Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection. Custom docker images with additional tools are available from here:

https://github.com/waikato-datamining/paddledetection

Prerequisites#

Make sure you have the directory structure created as outlined in the Prerequisites.

Data#

In this example, we will use the American Sign Language Letters dataset, which consists of sets of images of hands, one per letter in the English alphabet (26 labels).

Download the dataset from the following URL into the data directory and extract it:

https://datasets.cms.waikato.ac.nz/ufdl/data/american-sign-language-letters/american-sign-language-letters-voc.zip

Once extracted, rename the voc directory to sign-voc.

Now we have to convert the format from VOC XML into MS COCO. We can do this by using the image-dataset-converter library. At the same time, we can split the dataset into train, validation and test subsets.

From within the applied_deep_learning directory, run the following command:

docker run --rm -u $(id -u):$(id -g) \
  -v `pwd`:/workspace \
  -t waikatodatamining/image-dataset-converter:0.0.4 \
  idc-convert \
    -l INFO \
    from-voc-od \
      -i "/workspace/data/sign-voc/*.xml" \
    to-coco-od \
      -o /workspace/data/sign-coco-split/ \
      --category_output_file labels.txt \
      --split_names train val test \
      --split_ratios 70 15 15

Training#

For training, we will use the following docker image:

waikatodatamining/paddledetection:2.8.0_cuda11.8

Inference is possible without a GPU as well (though much, much slower). For utilizing a CPU you can use the following docker image:

waikatodatamining/paddledetection:2.8.0_cpu

The training script is called paddledet_train, for which we can invoke the help screen as follows:

docker run --rm \
  -t waikatodatamining/paddledetection:2.8.0_cuda11.8 \
  paddledet_train --help

It is good practice creating a separate sub-directory for each training run, with a directory name that hints at what dataset and model were used. So for our first training run, which will use mainly default parameters, we will create the following directory in the output folder:

sign-paddle-yolov3

Before we can train, we will need to obtain and customize a config file. Within the container, you can find example configurations for various architectures in the following directory:

/opt/PaddleDetection/configs

Using the paddledet_export_config command, we can expand and dump one of these configurations for our own purposes:

docker run --rm \
  -u $(id -u):$(id -g) \
  --gpus=all \
  -v `pwd`:/workspace \
  -v `pwd`/cache/visualdl:/.visualdl \
  -v `pwd`/cache:/.cache \
  -v `pwd`/cache:/opt/PaddleDetection/~/.cache \
  -t waikatodatamining/paddledetection:2.8.0_cuda11.8 \
  paddledet_export_config \
  -i /opt/PaddleDetection/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml \
  -o /workspace/output/sign-paddle-yolov3/yolov3_mobilenet_v1_270e_coco.yml \
  -O /workspace/output/sign-paddle-yolov3 \
  -t /workspace/data/sign-coco-split/train/annotations.json \
  -v /workspace/data/sign-coco-split/val/annotations.json \
  --save_interval 10 \
  --num_epochs 50 \
  --num_classes 26

Kick off the training with the following command:

docker run --rm \
  -u $(id -u):$(id -g) \
  --shm-size 8G \
  --gpus=all \
  -v `pwd`:/workspace \
  -v `pwd`/cache/visualdl:/.visualdl \
  -v `pwd`/cache:/.cache \
  -v `pwd`/cache:/opt/PaddleDetection/~/.cache \
  -t waikatodatamining/paddledetection:2.8.0_cuda11.8 \
  paddledet_train \
  -c /workspace/output/sign-paddle-yolov3/yolov3_mobilenet_v1_270e_coco.yml \
  --eval \
  -o use_gpu=true

Export the model using the paddledet_export_model script:

docker run --rm \
  -u $(id -u):$(id -g) \
  --shm-size 8G \
  --gpus=all \
  -v `pwd`:/workspace \
  -v `pwd`/cache/visualdl:/.visualdl \
  -v `pwd`/cache:/.cache \
  -v `pwd`/cache:/opt/PaddleDetection/~/.cache \
  -t waikatodatamining/paddledetection:2.8.0_cuda11.8 \
  paddledet_export_model \
  -c /workspace/output/sign-paddle-yolov3/yolov3_mobilenet_v1_270e_coco.yml \
  --output_dir /workspace/output/sign-paddle-yolov3/inference

Predicting#

Using the paddledet_predict script, we can batch-process images placed in the predictions/in directory as follows (e.g., from our test subset): 

docker run --rm \
  -u $(id -u):$(id -g) \
  --shm-size 8G \
  --gpus=all \
  -v `pwd`:/workspace \
  -v `pwd`/cache/visualdl:/.visualdl \
  -v `pwd`/cache:/.cache \
  -v `pwd`/cache:/opt/PaddleDetection/~/.cache \
  -t waikatodatamining/paddledetection:2.8.0_cuda11.8 \
  paddledet_predict_poll \
  --model_path /workspace/output/sign-paddle-yolov3/inference/yolov3_mobilenet_v1_270e_coco \
  --device gpu \
  --label_list /workspace/data/sign-coco-split/train/labels.txt \
  --prediction_in /workspace/predictions/in \
  --prediction_out /workspace/predictions/out

Notes

  • The predictions get output in OPEX JSON format, which you can view the predictions with the ADAMS Preview browser:

Example prediction

Screenshot

Screenshot