A study of how erroneous training data affects the performance of deep learning systems for semantic segmentation
usage: model_runner.py [-h] [-n NEW] [-e EXISTING] [-b BATCH_SIZE]
[-d DATASET] [-p PERTURBATION] [-g GPU_INDEX]
[-m MODEL] [-t [TESTING]] [-v [VISUALIZE]]
Run a training or testing round for our Labels 2018 Submission
optional arguments:
-h, --help show this help message and exit
-n NEW, --new NEW A name for a new training session from random
initialization
-e EXISTING, --existing EXISTING
A name for an existing training session to continue
-b BATCH_SIZE, --batch_size BATCH_SIZE
The batch size to use during training
-d DATASET, --dataset DATASET
The dataset to train/test on either lis or psd
-p PERTURBATION, --perturbation PERTURBATION
The type of perturbations to do to the training data
-g GPU_INDEX, --gpu GPU_INDEX
The GPU to limit this training run to.
-m MODEL, --model MODEL
The architecture to use for this round
-t [TESTING], --testing [TESTING]
Whether this round will be a testing round
-v [VISUALIZE], --visualize [VISUALIZE]
Whether to visualize
callbacks
A directory of callbacks used during training
- TopNSaver: Saves top N models during training according to specified metric
- VizPreds: Visualizes predictions made by model during training
Models
A directory of files which define and compile the models for training
viz
Put an x,y .npy pair here and it will visualize predictions on this data during training
- UNet Implementation Courtesy of divamgupta
- FCN8 Implementation Courtesy of divamgupta
- SegNet Implementation Courtesy of ykamikawa