sanjana7395 / static_quantization

Post-training static quantization using ResNet18 architecture

Post-training Static Quantization (Pytorch)

This project perform post-training static quantization in Pytorch using ResNet18 architecture.

Configuration of Project Environment

1. Clone the project.
2. Install packages required.
3. Run the notebook.

Setup procedure

1. Clone project from GitHub.
   Change to the directory static_quantization.

2. Install packages
   In order to reproduce the code install the packages

       pip install torch==1.5.1 
       pip install torchvision==0.6.1 
       pip install matplotlib
   

3. Run the notebook.
   In this notebook we use MNIST data set for experimenting.

Results

Below are the results obtained on the test set for the models trained in the project.

NOTE
The results obtained are system specific. Due to different combinations of the neural network cudnn library versions and NVIDIA driver library versions, the results can be slightly different. To the best of my knowledge, upon reproducing the environment, the ballpark number will be close to the results obtained.

Models	Accuracy (%)	Memory size (MB)	Inference time (ms)
Original	99.28	44.76	15.06
Quantized	99.26	11.20	3.64