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iquaflow-airplane-yolov5-use-case

Airplanes object detection on different levels of image compression.

To request the dataset send an email to: [email protected].

Paper availabe here.


To reproduce the experiments:

  1. git clone [email protected]:satellogic/iquaflow-airplane-yolov5-use-case
  2. cd iquaflow-airplane-yolov5-use-case
  3. Then build the docker image with make build.(***) This will also download the dataset and weights
  4. In order to execute the experiments:
    • make dockershell (*)
    • Inside the docker terminal execute python ./iqf-usecase.py
  5. Start the mlflow server by doing make mlflow (*)
  6. Notebook examples can be launched and executed by make notebookshell NB_PORT=[your_port]" (**)
  7. To access the notebook from your browser in your local machine you can do:
    • If the executions are launched in a server, make a tunnel from your local machine. ssh -N -f -L localhost:[your_port]:localhost:[your_port] [remote_user]@[remote_ip] Otherwise skip this step.
    • Then, in your browser, access: localhost:[your_port]/?token=IQF

Notes

  • The results of the IQF experiment can be seen in the MLflow user interface.
  • For more information please check the IQF_expriment.ipynb or IQF_experiment.py.
  • There are also examples of dataset Sanity check and Stats in SateAirportsStats.ipynb
  • The default ports are 8888 for the notebookshell, 5000 for the mlflow and 9197 for the dockershell
  • (*) Additional optional arguments can be added. The dataset location is:

    DS_VOLUME=[path_to_your_dataset]

  • To change the default port for the mlflow service:

    MLF_PORT=[your_port]

  • (**) To change the default port for the notebook:

    NB_PORT=[your_port]

  • A terminal can also be launched by make dockershell with optional arguments such as (*)
  • (***) Depending on the version of your cuda drivers and your hardware you might need to change the version of pytorch which is in the Dockerfile where it says:

    pip3 install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html.

  • (***) The dataset is downloaded with all the results of executing the dataset modifiers already generated. This allows the user to freely skip the .execute as well as the apply_metric_per_run which take long time.

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Airplane detection using Yolov5 on Satellogic images

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