Yolov10

Yolov10 represents the implementation of YOLOv10: Real-Time End-to-End Object Detection. Custom docker images with additional tools are available from here:

https://github.com/waikato-datamining/pytorch/tree/master/yolov10

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 dataset10.yaml file, 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.csv file into a numbered list using the following command:

grep -v "Index" labels.csv | sed s/","/": "/g | sed s/^/"  "/g

Training

For training, we will use the following docker image:

waikatodatamining/pytorch-yolov10:2024-06-23_cuda11.7

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

waikatodatamining/pytorch-yolov10:2024-06-23_cpu

The training script is called yolov10_train, for which we can invoke the help screen as follows:

docker run --rm -t waikatodatamining/pytorch-yolov10:2024-06-23_cuda11.7 yolov10_train help 

Since we will be performing transfer larning, we need to download a base model to use for training. Yolov10 offers different models, which differ in speed and accuracy. We will use the fastest one called yolov10n.pt ("nano") from the v1.1 release:

https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.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-yolov10

Since the image size should be a multiple of 32, we use 640 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-yolov10:2024-06-23_cuda11.7 \
  yolov10_train \
  model=/workspace/models/yolov10n.pt \
  data=/workspace/data/sign-yolo-split/dataset10.yaml \
  imgsz=640 \
  exist_ok=true \
  project=/workspace/output/ \
  name=sign-yolov10 \
  amp=false \
  batch=4 \
  epochs=20

Predicting

Using the yolov10_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-yolov10:2024-06-23_cuda11.7 \
  yolov10_predict_poll \
  --model /workspace/output/sign-yolov10/weights/best.pt \
  --prediction_in /workspace/predictions/in \
  --prediction_out /workspace/predictions/out

Notes

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

Example prediction

Exporting to ONNX (optional)

Before we can use our trained model, we will need to export it to ONNX format using the yolov10_export script:

docker run --rm \
  -u $(id -u):$(id -g) \
  --gpus=all \
  -v `pwd`:/workspace \
  -t waikatodatamining/pytorch-yolov10:2024-06-23_cuda11.7 \
  yolov10_export \
  model=/workspace/output/sign-yolov10/weights/best.pt \
  format=onnx \
  opset=13 \
  simplify

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

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.