RACNN

Radius Adaptive Convolutional Neural Net

This is an implementation of RACNN on Python, Keras, and TensorFlow. Radius adaptive CNN is a method that adopts different kernel sizes (or radii) based on the input content. α defines how much of the neighboring pixels are taken into account. α=0 and α=1 are equivalent to 1x1 and 3x3 convolutions.

The repository includes:

• Source code of RACNN for CPU and GPU for VGG16 and Resnet50
• Graphs for VGG16, Resnet50, VGG16-RACNN, and Resnet50-RACNN
• Test images
• Trained weights for COCO dataset
• Demo examples

Getting Started with Python

• racnn_cpu_demo is a simple demonstration of RACNN using CPU.
• racnn_gpu_demo is a simple demonstration of RACNN using NVIDIA GPU.
• keras_demo contains graphs and Keras implementations.
• results contains the most recent results.

Installation (Python)

1. Install dependencies

   pip3 install package [numpy, keras, opencv-python ...]

2. Clone this repository

3. Run setup from the racnn/libs directory

   • for CPU python3 setup.py install

   • for GPU python3 setup_gpu.py install

   or:

   python3 setup.py build and copy the compiled modules (.pyd for windows and .so for linux to your working directory)

Results

Results will be updated here

Weights and test data

weights and test data can be downloaded from racnn1.0