uavaustin / target-finder Goto Github PK
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License: MIT License
Python library for identifying AUVSI SUAS targets
License: MIT License
Use the GitHub Actions documentation here to create a GitHub Actions workflow for target finder model.
Create inference pipeline.
Currently we generate data by pasting multiple targets onto a large image, then slice that image up into training data.
This method leads to a problem where targets can be sliced, leading to omitting their existence in training data.
To fix this, create a script which pastes a single target onto slices of the larger background images.
Try to remove all the pep8 errors for style
Make skeleton for how targets are displayed on targets page.
See this link for a starting point. Cache the download of dependencies in the target-finder and target-finder-model builds
Currently, color classification is error prone
Investigate the cause for this and potential solutions.
See this for help.
Build a docker image that will be used to optimize a frozen tf graph on the Xavier.
Docker image needs tf < 2.0 and tf/tftrt
BLOCKED BY #47
Add multiprocessing using the the built in python library.
Refer to create_full_images.py for inspiration.
Refer to https://github.com/uavaustin/target-finder/tree/benchmarks for inspiration.
Implement a script that can be used to benchmark our pipeline and return useful analytics such as: classification time, detection time, and accuracy.
Use this resource for inspiration.
Review images from competition.
Edit the create_full_images.py script to add some variance (possibly Gaussian distribution) of colors to targets.
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