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PaddleDetection (instance segmentation)

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 Oxford Pets dataset, which consists of 37 different categories of cats and dogs.

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

https://datasets.cms.waikato.ac.nz/ufdl/data/oxford-pets/oxford-pets-adams.zip

Rename the adams directory to pets-adams.

To speed up training, we only use two labels: cat:abyssinian and dog:yorkshire_terrier. The label filtering and splitting it into train, validation and test subsets is done using image-dataset-converter:

docker run --rm -u $(id -u):$(id -g) \
  -v `pwd`:/workspace \
  -t waikatodatamining/image-dataset-converter:0.0.4 \
  idc-convert \
    -l INFO \
    from-adams-od \
      -i "/workspace/data/pets-adams/*.report" \
    filter-labels \
      --labels cat:abyssinian dog:yorkshire_terrier \
    discard-negatives \
    coerce-mask \
    to-coco-od \
      -o /workspace/data/pets2-coco-split \
      --categories cat:abyssinian dog:yorkshire_terrier \
      --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

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:

pets2-paddledet-maskrcnn

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/mask_rcnn/mask_rcnn_r50_1x_coco.yml \
  -o /workspace/output/pets2-paddledet-maskrcnn/mask_rcnn_r50_1x_coco.yml \
  -O /workspace/output/pets2-paddledet-maskrcnn \
  -t /workspace/data/pets2-coco-split/train/annotations.json \
  -v /workspace/data/pets2-coco-split/val/annotations.json \
  --save_interval 10 \
  --num_epochs 30 \
  --num_classes 2

Open the mask_rcnn_r50_1x_coco.yml file in a text editor and perform the following operations:

  • change base_lr to 0.0005

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/pets2-paddledet-maskrcnn/mask_rcnn_r50_1x_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/pets2-paddledet-maskrcnn/mask_rcnn_r50_1x_coco.yml \
  --output_dir /workspace/output/pets2-paddledet-maskrcnn/inference

Predicting#

Using the paddledet_predict_poll 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/pets2-paddledet-maskrcnn/inference/mask_rcnn_r50_1x_coco \
  --device gpu \
  --label_list /workspace/data/pets2-coco-split/train/labels.txt \
  --prediction_in /workspace/predictions/in \
  --prediction_out /workspace/predictions/out \
  --threshold 0.3 \
  --mask_nth 2

Notes

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

Example prediction

Screenshot Screenshot