In the folder "Evaluation-tools", you will find code to evaluate the performance of the trained network on the labeled data set (train and test images).
*For steps that require CUDA, if you are using the Docker container, run steps that say CUDA_VISIBLE_DEVICES.. inside the container. We run all the network analysis steps listed below in the container. If you evaluate the nework on a CPU then just ommit the CUDA_VISIBLE_DEVICES=0 command.
NETWORK EVALUATION:
$ CUDA_VISIBLE_DEVICES=0 python3 Step1_EvaluateModelonDataset.py #to evaluate your model [needs TensorFlow <1.2]
$ python3 Step2_AnalysisofResults.py #to compute test & train errors for your trained model
For more information on the output of these programs etc., check out the detailed walk-through with labeled example data.
Return to readme.