https://github.com/szymonrucinski/plan-doskonaly
React and TypeScript web app 🌩️ for labelling huge images 🖼️ datasets. Learn about movies and cinematography 🎥 while helping to build a huge open-source dataset for CNN 🧠
https://github.com/szymonrucinski/plan-doskonaly
ai data-labeling-tools frontend machienlearning react
Last synced: 4 months ago
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React and TypeScript web app 🌩️ for labelling huge images 🖼️ datasets. Learn about movies and cinematography 🎥 while helping to build a huge open-source dataset for CNN 🧠
- Host: GitHub
- URL: https://github.com/szymonrucinski/plan-doskonaly
- Owner: szymonrucinski
- Created: 2020-10-29T16:53:23.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-13T19:49:59.000Z (over 2 years ago)
- Last Synced: 2025-01-08T21:22:13.388Z (5 months ago)
- Topics: ai, data-labeling-tools, frontend, machienlearning, react
- Language: TypeScript
- Homepage: https://plan-doskonaly.netlify.app/
- Size: 20.2 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
---
## Movie Set Labelling App (Plan Doskonały)
### Introduction
[Plan Doskonały](https://plan-doskonaly.netlify.app) is a React Typescript web application for labelling images (shots from movies) as part of an engineering thesis by Szymon Ruciński. The goal of the project is to label frames from movies collected by my data-mining script.
A CNN model will be later trained on that data to classify different type of movie shots.
### How does it work?
Before you start, please visit the [learn section ](https://plan-doskonaly.netlify.app/learn) section to familiarize yourself with the types of movie shots. It explains in Polish all the types of shots and their definitions with examples.
### Quiz
Take the quiz consisting of 25 film frames and identify the correct type of set for each frame.
You will not be able to move on to the next question without submitting your answer.
Once all shots are classified and you agree to publish the results, the answers will be sent to the server.
The project currently has 156 thousand photos from 2504 videos that are waiting to be classified. Your help in correctly identifying the frames is invaluable and will greatly aid in training the AI model. Thank you for your support!