- Python 2.7
- Tensorflow 0.12
- Needs to be this specific version if you do not want to modify any code
- Tensorflow made changes to the name of the function that initializes variable tensors in v0.12
- Tensorflow made changes yet again to the function that initializes variable tensors in v1.0
- So if you run on another version of Tensorflow, you will need to make changes to the code.
- OpenCV 3.1.0
- Numpy
- Matplotlib
- Pandas
- Pillow
- h5py
- argparse
And for making use of the real-time camera app, you will need a computer with a webcam plugged in.
If you just want to try out the app then you can just run the following:
python live_predictor.py
This will launch an application that displays the captured video feed from the default webcam on the computer, and overlays the predicted bounding boxes and digits on the screen in real time.
If you want to use the existing workflow and existing deep learning architectures included to train a new model from scratch, then you can follow the following steps.
The data used for training the models in this project are not included. The data can be downloaded from the following links.
- http://ufldl.stanford.edu/housenumbers/train.tar.gz
- http://ufldl.stanford.edu/housenumbers/test.tar.gz
- http://ufldl.stanford.edu/housenumbers/extra.tar.gz
The scripts assume that the contents of these tar files are extracted within a subdirectory "data". This should result in the three subdirectories:
- data/train
- data/extra
- data/test
To process the data to be used by the neural network, simply run the following in the command line:
python create_data_pickles.py -i "data"
This will create several pickle files within the data subdirectory that will
be used by the neural network directly.
python trainer.py -m a -e 10 -b 32 -a 0.001 -o "A_01"
| argument | what it is | possible values |
|---|---|---|
| -m | model architecture | "a", "b", "c", or "d" |
| -e | number of epochs | any integer value |
| -b | batch-size | any integer value |
| -a | alpha learning rate | a float between 0-1 |
| -o | output name * | any string that could legally be used as a directory name. |
- * the output name will be used as a place where all snapshots, evaluation files, and visualisations for the model will be stored, in a subdirectory
results/output_name
python tester.py
This will print out the Per Digit Accuracy, Whole Number Accuracy, and Intersect
of Union score on the test dataset. It will also save images of the following
things in the imgs subdirectory:
- Grid of predictions on test set
- Grid of worst performing bounding box predictions
- Grid of incorrect digit predictions
python live_predictor.py
Will launch an application that displays the captured video feed from the default webcam on the computer, and overlays the predicted bounding boxes and digits on the screen in real time.
NOTE: You may need to change some of the code to get it to load up the checkpoint files for your trained model. By default it is looking for a model called "A_02" using the model_a architecture, which is the pre-trained model that is included with this repo.
Only the The checkpoint files for the best performing model (model A) are
packaged here in the interest of keeping file size to a minimum. These are
located in results/A_02/checkpoints.