Skip to content

Latest commit

 

History

History
121 lines (101 loc) · 4.81 KB

README.md

File metadata and controls

121 lines (101 loc) · 4.81 KB

similar-sneakers

Author: Timofey Vilkov

Supervisor: Panchuk Georgii

Formulation of the problem: Creating a text2image platform based on multimodal models for searching similar sneakers by user text description.

Roadmap:

  • Searching sites with large number of items (01.01.2023 — 01.03.2023)
    • analyze the quality of content
    • estimate complexity of parsing
  • Implement parser for scraping image and text data from sneaker platforms (05.03.2023 — 15.03.2023)
    • write parser
    • parse and scrape data
    • extract data from raw responses
    • collecting images
  • EDA (15.03.2023 — 20.03.2023)
  • Data subsample labeling (20.03.2023 — 27.03.2023)
  • Research multimodal models, сhoosing the best one (27.03.2023 — 20.04.2023)
    • define metrics for decision making
    • evaluation of selected models using labelled subset of the data
    • choosing the best model
  • ML system design (27.03.2023 — 15.04.2023)
  • ML system implementation (15.04.2023 — 15.05.2023)
    • writing code for inference
    • hosted app on the server
    • create bot
    • create base flow for interact with user
    • logging base metrics about load on the model
  • Inference optimization (15.05.2023 — 31.05.2023)

Data description: Data: parsed shoe items from https://www.ssense.com/.

Used endpoints:

Raw data: raw_data_urls.json

CSV with urls: url_dataset.csv

Default image resolution: 940x960

Dataset size: 42240 images

Features:

  • brand
  • name (not always unique, e.g. Black Leather Loafers)
  • gender
  • categories
  • images
  • seoKeyword
  • priceByCountry
  • url

Service description: Telegram bot with comfortable UX for user

Stack: TBD

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
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── 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
├── sneakers           <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── make_dataset.py
│   │   ├── curl_params.py
|   |   └── ssense_scrapings.ipynb
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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