kzaleskaa / depth-estimation-with-compression

Development of a depth estimation model based on a UNET architecture - connection of Bi-directional Feature Pyramid Network (BIFPN) and EfficientNet.

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Depth Estimation (BiFPN + EfficientNet)

Description

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.

Installation

Pip

# 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

Conda

# 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

How to run

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