Skip to content

Yolov5

Yolov5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods. Custom docker images with additional tools are available from here:

https://github.com/waikato-datamining/pytorch/tree/master/yolov5

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 YOLO. 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-yolo-od \
      -o /workspace/data/sign-yolo-split \
      --labels labels.txt \
      --labels_csv labels.csv \
      --split_names train val test \
      --split_ratios 70 15 15

Finally, download the dataset.yaml file and place it in the sign-yolo-split directory. It contains information about the dataset directory, the splits and the class labels.

If you want to adapt this configuration for different labels, then you can automatically transform the labels.txt file into a quoted list using the following command:

cat labels.txt | sed s/,/\',\'/g | sed s/^/\'/g | sed s/$/\'/g

Training#

For training, we will use the following docker image:

waikatodatamining/pytorch-yolov5:2022-11-05_cuda11.1

If you do not have a GPU, you can use the CPU-only image:

waikatodatamining/pytorch-yolov5:2022-11-05_cpu

The training script is called yolov5_train, for which we can invoke the help screen as follows:

docker run --rm -t waikatodatamining/pytorch-yolov5:2022-11-05_cuda11.1 yolov5_train --help

Instead of using config files, we can just tweak parameters via command-line options.

However, we still need to download a base model to use for training. Yolov5 offers different models, which differ in speed and accuracy. We will use the medium one called yolov5m.pt from the v6.2 release:

https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt

Download it and place it in the models directory.

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-yolov5

Since the image size should be a multiple of 32, we use 416 for this experiment.

Kick off the training with the following command:

docker run --rm \
  -u $(id -u):$(id -g) \
  --shm-size 8G \
  --gpus=all \
  -v `pwd`:/workspace \
  -t waikatodatamining/pytorch-yolov5:2022-11-05_cuda11.1 \
  yolov5_train \
  --img 416 \
  --batch 16 \
  --epochs 50 \
  --data /workspace/data/sign-yolo-split/dataset.yaml \
  --weights /workspace/models/yolov5m.pt \
  --project /workspace/output \
  --name sign-yolov5 \
  --exist-ok

Exporting to ONNX#

Before we can use our trained model, we will need to export it to ONNX format using the yolov5_export script:

docker run --rm \
  -u $(id -u):$(id -g) \
  --gpus=all \
  -v `pwd`:/workspace \
  -t waikatodatamining/pytorch-yolov5:2022-11-05_cuda11.1 \
  yolov5_export \
  --weights /workspace/output/sign-yolov5/weights/best.pt \
  --img-size 416 416 \
  --include onnx

This will create a file called best.onnx in the output directory.

Predicting#

Using the yolov5_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) \
  --gpus=all \
  -v `pwd`:/workspace \
  -t waikatodatamining/pytorch-yolov5:2022-11-05_cuda11.1 \
  yolov5_predict_poll \
  --model /workspace/output/sign-yolov5/weights/best.onnx \
  --data /workspace/data/sign-yolo-split/dataset.yaml \
  --image_size 416 \
  --prediction_in /workspace/predictions/in \
  --prediction_out /workspace/predictions/out

Notes

  • By default, the predictions get output in ROI CSV format. But you can also output them in the OPEX JSON format by adding --prediction_format opex --prediction_suffix .json to the command.

  • You can view the predictions with the ADAMS Preview browser:

Example prediction

Screenshot

Screenshot

Troubleshooting#

  • If you are re-using a dataset that was used by another YolovX framework, you may get strange error messages when reading the data. This can be due to incompatible cache files that get generated to speed up loading the data. Make sure to remove all files in the labels directory that have a .cache extension.