From Immersive Visualization Lab Wiki
Participation Assignment (due October 27th at noon)
Answer these questions:
- What does your COCO demo do differently than the default demo?
- Show a sample output of your demo (can be a mock-up of what you want it to be)
Replication Checkpoint 1 (due October 30th at 11:59pm)
Write an introduction including:
- An explanation of the problem being investigated: how can an image classification algorithm be trusted to give the correct results?
- A brief explanation of the context of the problem and why it’s interesting.
- A description of the type of data for which the method is appropriate: basic description of observed data used in the investigation (COCO dataset) and why it’s appropriate for addressing the problem.
- Modify the COCO demo to create your own that is different than the default demo.
- Your code should be turned in via GitHub. It should:
- conform to the template structure discussed in lecture,
- contain a rudimentary data ingestion pipeline,
- include documentation both in your README.md, describing the purpose of the code, its contents, and how to run it.
- be runnable via the command python run.py data. Include a data-params.json file in the config directory, which specifies any data-input locations.
- include the COCO dataset location in your data-params.json
For both the Report and Code Portion, write in a Canvas comment listing what tasks each group member was responsible for.