Python >= 3.8 (and maybe >=3.7 could also works but I'm not sure).
Install them, for example via pip in a terminal:
pip install --requirement ./requirements.txt
Or any virtualenv or something that you want.
if you have trouble installing scikit with pycharm :
python -m pip install scikit-learn==0.23.2
Install the Traffic sign dataset, unzip and put it in the folder ./data/traffic-sign
.
Then, run the script traffic_sign_data_cleaning.py
. This script will restructure the dataset to allow our models to work easier (it does some label to folder mapping).
Follow instructions here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md
The base tutorial: https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/object_detection_tutorial.ipynb
Make sure that you have Tensorflow 2 installed, Tensorflow 1 won't work. If have followed the previous step, then it should be fine.
- Description
- This model uses 80 categories from the COCO image dataset to detect objects.
- References
- Tensorflow Object Detection API.
- Paper by Microsoft: COCO: Common Objects in Context
- COCO website.
- Sources
- We use a detection model pre-trained on the COCO 2017 dataset by Tensorflow. You can find a list of models here.
- We use the SSD mobilenet v2 coco17 because only SSD models are supported by Tensorflow Lite, which could be handy in the future and this is a very performant model.
We trained a model to recognize traffic signs. The model is a MobilenetV2 trained from scratch. That means we didn't use transfer learning based on the COCO dataset (or anything else).
- Description
- This model uses 43 traffic sign categories from the GTSRB dataset.
- References
- Tensorflow 2.
- MobilenetV2 model.
- Sources