/traffic-sign-classification-uff-tensorrt

Traffic sign classification training and conversion code to uff - TensorRT

Primary LanguagePython

Traffic sign classification

The purpose of this project is to train a traffic sign classification model for an advanced driver-assistance system. More about this project. The inference code was integrated in this repository.

Step 1: Initialize environment

Create anaconda enviroment:

conda create --name <ENVIROMENT_NAME> python=3.6

Activate created environment and install all requirements:

pip install requirements.txt

Step 2: Train the models

  • Prepare dataset like below structure:

Dataset preparation

  • Train the model:
python train.py

Step 3: Convert to UFF

  • Modify model paths in convert_h5_to_pb.py and convert_pb_to_uff.py.

  • Convert .h5 model to .pb, and finnally .uff:

pip install requirements-h5-to-uff.txt
python convert_h5_to_pb.py
python convert_pb_to_uff.py