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DeepStack utils.

Note I have moved the training data and sample model to its own repo to keep this one from becoming too large as I expand the model.

DeepStack and Utils Setup

Utils

fullTest

A quick Python 2.7 test to make sure your setup is working. Including the 4 custom models in the DeepStack documentation. Note their online documentation has a lot of errors in it.

startDeepStack.sh

Shell script for starting DeepStack with params needed for running tests.

updateDeepStack.sh

Shell script for updating then starting DeepStack with params needed for running tests.

testTorch.bat and testTorch.py

Test that Torch setup is working and using your GPU ready to train.

localColab.bat

Bat file to bring up my Jupyter Notebook for training with local GPU.

Quick Label

Runs pics in newPicPath against all the installed object detect models to generate tag files similar to what the labelImg would produce along with a list of all found objects (classes.txt).

Convert and Merge example

Example of the thought process in selecting images from your cameras and merging with datasets from the web.

chkClasses.py

Program for merging the output of quickLabel (after being verified) with the data in trainPath

rmClasses.py

Program for removing poor performing or conflicting classes from the data in trainPath

trainTest.py

Runs all the training images against the model to see where weakness are to help in adjusting image maps and or removing classes and or images that reduce accuracy.