Tiny, Quantized Neural Network, orignially based on ResNet8, trained to recognize pokemon. Originally developed/optimized for the TinyMLPerf v0.7/1.0 Image Classification Benchmark, using the CIFAR-10 Dataset.
To Train a given model:
python train_pokemon.py -c config/<config>.yml
where <config> is one of the yml files in the config directory. RN06_Poke10.yml is what was used for the DEFCON30 Webcam Demo. If you are intending to retrain this model for deployment on a Pynq-Z2 or similar FPGA platform (Xilinx Zynq 7020), it is reccomended to use RN06 if you need to have other large/major IP blocks in your firmware (such as HDMI, etc.) and RN07 if the neural network is the only thing you intend to run on the PL/Fabric.
Multiple pretrained models are in this repo. The one used in the DEFCON30 Webcam Demo is trained_model/deploy/resnet_v1_eembc_RN06_bilinear/small_model_best.h5, though most variations in trained_model/deploy are simple variations on the preprocessing of the training set.
The pokemon datasets used in this training are mentioned in data/sources.txt, with the training dataset(s) availible on Kaggle, and the test dataset needing to be scraped. The (very janky, cobbled together, annoying to use) tool can be found in this repo.
This repo does not contain the scripts required to convert and deploy this model onto an FPGA. They are located in this repo.