cnn-planesnet

This project is about compressed CNNs for airplane classification in satellite images using average-percentage-of-zeros-based parameter pruning and INT8 weight quantization. It also evaluates the impact of epochs and the batch size on the accuracy for good baseline parameters and experiments with modified CNN architectures.

Project overview

The measurements in the respective folders contain the measurement data, graphs and model files.

Main program:

• main: Splits the dataset, trains the model and compresses it with pruning and quantization.
• parameters: The parameters contained in this file are read by both main.py and the batch execution files. They contain settings for epochs, batch size, pruning and quantization, e.g., the minimum / maximum / current / increment value.

Batch execution:

• batch_execute: Allows to vary the epochs and batch size both on their own and one after another. You need to set the mode in the source code manually.
• batch_quantization: Varies epochs and batch sizes while batch quantizing models found in a specified directory.

CNN architecture:

• cnn_definitions: Defines the baseline CNN for the experiment (cnn1). Variations of cnn1 can be performed in 6 lines in cnn2. My attempt to create my own CNN from the scratch is contained in cnn3.
  • givenCNN: Contains the results for varying the batch size and the number of epochs for cnn1.
  • modifiedCNN: This folder captures what happens when you uncomment certain lines of 1-6 of cnn2, giving an insight on how the results change depending on the layers included.
  • ownCNN: Measurements for my own CNN (cnn3). These are probably not relevant because I have stopped varying my CNN when I noticed that changing an already working CNN is much less effort, but yields better results.

CNN compression:

• cnn_utils: Contains the implementation of pruning and quantization. The two folders in the quantize method need to be adjusted.
  • pruning: folder with pruning results
  • quantization: folder with quantization results

Sample commands

Download the planesnet.json dataset, rename it to planesnet.json and place it in the project's root directory. This might require creating a Kaggle account.

Please refer to the following picture as to which packages should be installed.

For the quantization to work, you need to have tf_nightly installed. tensorflow-gpu is also needed, including the appropriate cuDNN and Cuda drivers (for NVIDIA GPUs).

Now you can run the Python files main.py, batch_execute.py and batch_quantization.py. There are no parameters, since the files access and adjust parameters.json to get the configuration.