Application of machine learning models to recognise objects from the CIFAR-10 dataset. This work uses the TensorFlow framework to build, train, and deploy models to produce real-time object classifications.
 |
 |
| Batch Normalised Model Confusion Matrix | Batch Normalised Model Inference Timings with 128 batch size |
This project utilises the TensorFlow framework to create and train machine learning models. Please ensure that the TensorFlow is properly installed on your system. Please refer to the TensorFlow documentation.
Important
This work is compatible to run on both CPU and GPU. To improve performance, it is advised to use the GPU for training models. Please ensure that the GPU drivers are up to date and the necessary development toolkits are installed on your system. For NVIDIA GPUs, please install the CUDA Toolkit and cuDNN SDK.
To run the application, run the following commands.
Create a Python virtual environment
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python RealtimeClassification.py