DeepPicarMicro

Demo

Model Training

Dependencies

In order to train the DeepPicar models the following Python modules are required:

$ pip install tensorflow numpy pandas scikit-learn opencv-python tensorflow-model-optimization

The model architecture can be set by changing the modelname field in any params.py:

modelname = "pilotnet"

For example, to train the PilotNet model with Depthwise Separable layers, change "pilotnet" to "pilotnet_depthwise".

Dataset

We assume that all datasets are located in a directory named "Datasets/" with the following structure:

```
Dataset
|____<dataset #1>
|	|____<epoch #1>
|	|	|	out_video.avi
|	|	|	out_key.avi
|	|____<epoch #2>
|	|	...
|____<dataset #2>
|	...
```

The dataset used for training can then be changed in any params.py::

dataset="<dataset #1>"	# Replace with actual dataset name

The datasets used to train the models in the paper can be found at:

• Real-world dataset: https://drive.google.com/file/d/1Fjwy-dLDp5sNilPTUMXUkmnUF1SrXDOl/view
• Udacity simulator dataset: https://drive.google.com/file/d/1WW0r-Zx0_sPULpsV_44i-M1zZhQwX6ao/view

Keras/TFLite/TFLiteMicro Model Creation

To create both Keras and TFLite models for the chosen architecture, run the following:

$ python quant-train.py

To create a TFLiteMicro compatible model representation, run the following command inside the desired models directory:

$ xxd -i quant-model.tflite <model-name>.cc

Where is the name of the new file.

Neural Architecture Search (NAS)

To perform a NAS for a given model backbone/dataset combination, the following commands can be run:

$ python filter_models.py <max_MACs>
$ python train_pass.py
$ ./create-cc.sh <dataset_name>

Note: The NAS process can be resource intensive in terms of system and GPU memory. In the paper, we used a PC with 64GB of system memory and a GPU with 10GB of memory.

Pico

Detailed instructions can be found in the Pico directory.

Udacity Simulator

Detailed instructions can be found in the UdacitySim directory.