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Computer vision II: object detection and instance segmentation

    • Thursday, November 21, 2024, 1:30-4:30pm CT
      • Lead: ARS SCINet Office
      • Prerequisites:
        • Familiarity with basic machine learning concepts. The workshop on November 20 will provide this background, if needed.
        • Familiarity with basic computer vision concepts (e.g., an understanding of how image data are structured in computer memory). The morning computer vision workshop will provide this background.
        • Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.

In this workshop, participants will learn the key concepts and techniques needed to use modern, deep learning-based computer vision methods for object detection and instance segmentation. Learners will practice training and evaluating state-of-the-art computer vision models on custom image datasets.

This workshop is intended as a continuation of “Computer vision I: introduction and image classification”, but participants do not need to take the earlier workshop if they already have a basic knowledge of machine learning and computer vision concepts.

Tutorial setup instruction

Steps to prepare for the tutorial:

  1. Login to Atlas Open OnDemand at https://atlas-ood.hpc.msstate.edu/. For more information on login procedures for web-based SCINet access, see the SCINet access user guide.

  2. Open a command-line session by clicking on “Clusters” -> “Atlas Shell Access” on the top menu. This will open a new tab with a command-line session on Atlas’s login node.

  3. Request resources on a compute node by running the following command:

     srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash

  4. Create and/or update your workshop working directory and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.

     mkdir -p /90daydata/shared/$USER/computer_vision2
 cd /90daydata/shared/$USER/computer_vision2
 cp /project/ai_forum/computer_vision2/*.ipynb .
 cp /project/ai_forum/computer_vision2/*.py .

  5. Setup the kernel for JupyterLab. You will create a kernel called computer_vision_2_env to access from JupyterLab Server. Run the following commands to activate the workshop’s virtual environment and create a new kernelspec from it:

     source /project/ai_forum/computer_vision2/computer_vision_2_env/bin/activate
 ipython kernel install --name "computer_vision_2_env" --user

  6. Stop the interactive job on the compute node by running the command:

     exit

  7. Launch a JupyterLab Server session. Under the Interactive Apps menu, select JupyterLab Server. Specify the following input values on the page:

    • Account: scinet_workshop1
    • Partition: gpu-a100-mig7
    • QOS: normal 14-00:00:00
    • Number of hours: 4
    • Number of nodes: 1
    • Number of tasks: 2

    • Additional Slurm Parameters:

      --reservation=forum-gpu --gres=gpu:1 --mem=32G --ntasks-per-node=2

    • Working Directory:

      /90daydata/shared/${USER}/computer_vision2

    Click Launch. The screen will update to the Interactive Sessions page. When your Jupyter session is ready, the top card will update from Queued to Running and a Connect to JupyterLab Server button will appear. Click Connect to JupyterLab Server.

  8. Open the computer_vision_2.ipynb notebook.

  9. Select the computer_vision_2_env kernel for the notebook.