![](https://private-user-images.githubusercontent.com/62251989/321034012-747c72d8-e096-4113-9951-5886213187bc.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjQwMTQ5ODgsIm5iZiI6MTcyNDAxNDY4OCwicGF0aCI6Ii82MjI1MTk4OS8zMjEwMzQwMTItNzQ3YzcyZDgtZTA5Ni00MTEzLTk5NTEtNTg4NjIxMzE4N2JjLnBuZz9YLUFtei1BbGdvcml0aG09QVdTNC1ITUFDLVNIQTI1NiZYLUFtei1DcmVkZW50aWFsPUFLSUFWQ09EWUxTQTUzUFFLNFpBJTJGMjAyNDA4MTglMkZ1cy1lYXN0LTElMkZzMyUyRmF3czRfcmVxdWVzdCZYLUFtei1EYXRlPTIwMjQwODE4VDIwNTgwOFomWC1BbXotRXhwaXJlcz0zMDAmWC1BbXotU2lnbmF0dXJlPWQ5MzFjODhkNzM5MjAwODY1NTYyMjMyMjRkYWU0ZDIyOTU5YzAzZjBlNzgzZDI5Y2M0NTY5Nzc5OWM4NjFmMGYmWC1BbXotU2lnbmVkSGVhZGVycz1ob3N0JmFjdG9yX2lkPTAma2V5X2lkPTAmcmVwb19pZD0wIn0.mBLbloNSVwJM_MsKbxWKZHJW25-ajPYF2j6bTUMSPJk)
This project entails the development and optimization of a depth estimation model based on a UNET architecture enhanced with Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet components. The model is trained on the NYU Depth V2 dataset and evaluated on the Structural Similarity Index (SSIM) metric.
# clone project
git clone https://github.com/kzaleskaa/depth-estimation-with-compression
cd depth-estimation-with-compression
# [OPTIONAL] create conda environment
conda create -n myenv python=3.11
conda activate myenv
# install pytorch according to instructions
# https://pytorch.org/get-started/
# install requirements
pip install -r requirements.txt
# clone project
git clone https://github.com/kzaleskaa/depth-estimation-with-compression
cd depth-estimation-with-compression
# create conda environment and install dependencies
conda env create -f environment.yaml -n myenv
# activate conda environment
conda activate myenv
Train model with default configuration
# train on CPU
python src/train.py trainer=cpu
# train on GPU
python src/train.py trainer=gpu
Train model with chosen experiment configuration from configs/experiment/
python src/train.py experiment=experiment_name.yaml
You can override any parameter from command line like this
python src/train.py trainer.max_epochs=20 data.batch_size=64
The base model was trained for 25 epochs. QAT was performed for 10 epochs.
Baseline and Fuse
Method | test/ssim (Per tensor) | model size (MB) (Per tensor) |
---|---|---|
baseline | 0.778 | 3.53 |
fuse | 0.778 | 3.45 |
PTQ, QAT, and PTQ + QAT (Per tensor and Per channel)
Method | test/ssim (Per tensor) | model size (MB) (Per tensor) | test/ssim (Per channel) | model size (MB) (Per channel) |
---|---|---|---|---|
ptq | 0.6480 | 0.96791 | 0.6518 | 0.9679 |
qat | 0.7715 | 0.96791 | 0.7627 | 0.9681 |
ptq + qat | 0.7724 | 0.96899 | 0.7626 | 0.9692 |