Crowd Counting

Crowd Counting using Xception. The repository presents a solution for Crowd Counting problem evaluated on Mall Dataset. Here we use pretraining on Shanghai Tech Dataset according to method mentioned in the book (Aurélien Géron. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. ” O’Reilly Media, Inc.”, 2022). As the starting point, we use Xception model pretrained on Imagenet Dataset.

This code is my contribution for following project: https://github.com/Di40/CrowdCounting_Xception_CSRNet/tree/main

Usage

Downloading data

First ensure that kaggle package has been installed on your system. Also generate your API token and put at at ~/.kaggle/kaggle.json. The procedure is described here: https://www.kaggle.com/docs/api Next download datasets by using src/download_data.sh

bash download_data.sh

Fit Xception and evaluate scores

Run training by using src/train.py

python train.py --model_config_path ../configs/model_config.yaml

Project Organization

├── LICENSE
├── Makefile                <- Makefile with commands like `make data` or `make train`
├── README.md               <- The top-level README for developers using this project.
├── data                    <- Datasets for training.
│
├── docs                    <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                  <- Trained and serialized models.
│
├── notebooks               <- Jupyter notebooks.
│
├── references              <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                 <- Project report.
│
├── requirements.txt        <- The requirements file for reproducing the analysis environment, e.g.
│                               generated with `pip freeze > requirements.txt`
│
├── setup.py                <- makes project pip installable (pip install -e .) so src can be imported
├── src                     <- Source code for use in this project.
│   ├── __init__.py         <- Makes src a Python module
│   │
│   ├── data                <- Tools for data manipulation
│   │   └── data.py
│   │
│   ├── models              <- Tools for modeling
│   │   └── model.py
│   │
│   ├── unzip.py            <- Script to unzip data to data folder
│   │
│   ├── download_data.sh    <- Script to download the datasets
│   │
│   └── train.py            <- Script to train the models
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience