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"German Traffic Sign Recognition Benchmark (GTSRB)\n",
"=================================================\n",
"\n",
"---\n",
"Introduction au Deep Learning (IDLE) \n",
"S. Aria, E. Maldonado, JL. Parouty \n",
"CNRS/SARI/DEVLOG - 2020\n",
"\n",
"Objectives of this practical work\n",
"---------------------------------\n",
" \n",
"Traffic sign classification with **CNN**, using Tensorflow and **Keras** \n",
"\n",
"\n",
"Prerequisite\n",
"------------\n",
"\n",
"Environment, with the following packages :\n",
" - Python 3.6\n",
" - numpy\n",
" - Tensorflow 2.0\n",
" - scikit-image\n",
" - scikit-learn\n",
" - Matplotlib\n",
" - seaborn\n",
" \n",
"You can create it from the `environment.yml` file :\n",
"```\n",
"# conda env create -f environment.yml\n",
"```\n",
"To manage conda environment see [there](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) \n",
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"\n",
"About the dataset\n",
"-----------------\n",
"\n",
"Name : [German Traffic Sign Recognition Benchmark (GTSRB)](http://benchmark.ini.rub.de/?section=gtsrb) \n",
"Available [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html) \n",
"or on **[kaggle](https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)** \n",
"\n",
"A nice example from : [Alex Staravoitau](https://navoshta.com/traffic-signs-classification/) \n",
"\n",
"In few words :\n",
" - Images : Variable dimensions, rgb\n",
" - Train set : 39209 images \n",
" - Test set : 12630 images\n",
" - Classes : 0 to 42\n",
"\n",
"Episodes\n",
"--------\n",
" \n",
"**[01 - Preparation of data](01-Preparation-of-data.ipynb)**\n",
" - Understanding the dataset\n",
" - Preparing and formatting data\n",
" - Organize and backup data\n",
" \n",
"**[02 - First convolutions](02-First-convolutions.ipynb)**\n",
" - Read dataset\n",
" - Build a model\n",
" - Train the model\n",
" - Model evaluation\n",
" "
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