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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A deep neural network based self-driving car, that combines Lego Mindstorms NXT with the computational power of a Raspberry Pi 3.
| Date | Stars |
|---|---|
| 2026-07-24 | 755 |
| 2026-07-25 | 755 |
| 2026-07-28 | 755 |
| 2026-07-30 | 755 |
| 2026-07-31 | 755 |
| 2026-08-06 | 755 |
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# Self-Driving Pi Car [](https://travis-ci.org/felipessalvatore/self_driving_pi_car) [](https://github.com/felipessalvatore/self_driving_pi_car/blob/master/LICENSE) ## Introduction Self-Driving Pi Car is a deep neural network based self-driving car, that combines Lego Mindstorms NXT with the computational power of a Raspberry Pi 3. This repository was created by [Paula Moraes](https://github.com/paulaksm) and [Felipe Salvatore](https://github.com/felipessalvatore). <p align = 'center'> <img src = 'images/track.png' height = '270px'> </p> <p align = 'center'> Robot driving on a track </p> You can read more about the project on [Medium](https://medium.com/@project_m/self-drives-me-crazy-from-0-to-self-driving-car-in-150-hours-bf4f68d50d8a) ## Getting Started ### Install The first thing you need to do is to install all the libraries for the Raspberry Pi. To do so, open a terminal in Raspberry Pi and run: ``` $ cd raspi_utils/ $ bash install.sh ``` In the computer that you will perform the training -- protip: don't train the model in the Raspberry Pi! -- install all the requirements by runnig: ``` $ pip install -r requirements.txt ``` ## Usage **Attention:** in the master branch all python code is written for Python 2. If you would like to run this project in Python 3, please switch to the python3 branch of this repository. ### Collecting data Before doing any kind of training you need to collect the track data. So in the Raspberry Pi -- with the assembled robot -- run the data collection script: ``` $ cd self_driving/data_collection/ $ python DataCollector.py -n <images_folder_name> ``` Pressing `q` will stop execution and save all images and pickle file. Inside the folder `<images_folder_name>` there will be subdirectories organized by timestamps similar to `2018-02-17-23-27-02` with the collected `*.png` images. All the associated labels are saved in a pickle file `2018-02-17-23-27-02_pickle` in `<images_folder_name>`. Compress `<images_folder_name>` directory and export it from Raspberry Pi to other computer (using scp command, cloud, email, etc). ``` $ tar cvf <images_folder_name>.tar <images_folder_name> ``` **Attention:** please continue following the instructions in the computer that will be use for training. ### Generating npy and tfrecords Before generating tfrecords, you need to transform the untar `<images_folder_name>` containing all folders of images and pickles into a tuple of np.arrays. Running the following script will result in the creation of `<npy_files_name>_90_160_3_data.npy` and `<npy_files_name>_90_160_3_labels.npy` files: ``` $ cd self_driving/data_manipulation/ $ python img2array.py <images_folder_path> <npy_folder_path> <npy_files_name> ``` To generate tfrecords from `*.npy` and augment or manipulate (e.g. binarize) the data, run: ``` $ cd ../ml_training/ $ python generate_tfrecords.py <npy_data_path> <npy_labels_path> -n <name_tfrecords> ``` Resulting in `<name_tfrecords>_train.tfrecords`, `<name_tfrecords>_test.tfrecords` and `<name_tfrecords>_valid.tfrecords` files. ### Hyperparameters optimization **Attention:** all code in this section can be runned on both Python 2 and 3 with TensorFlow 1.2.1 (and above) and with GPU support, if possible. Now it's time to test different architectures, learning rates and optimizers, in the hopes of improving accuracy. #### Best architecture search Running the following script will create `architecture_results.txt` file with the results for a given configuration passed through optional arguments. ``` $ python best_architecture.py -n <name_tfrecords> ``` #### Best learning rate search Running the following script will create `learning_rate_results.txt` file with the results for a given configuration passed through optional argument
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d7c11aa41dacb15b, topic:self-driving-car, readme:manipulation, name:self-driving
matched fp:d7c11aa41dacb15b, topic:tensorflow