phatv1/HCMC-AIC2024-test

https://aichallenge.hochiminhcity.gov.vn/

0

stars

36

commits

Jupyter Notebook

primary language

Oct 10, 2024

updated

README

HCMC-AIC2024

Joining forces with innovators and AI enthusiasts, this project is a dynamic collaboration aimed at crafting a cutting-edge event-retrieval system, proudly participating in the Ho Chi Minh AI Challenge 2024.

Table of Contents

📍 Overview

Welcome! This project is a collaborative endeavor to develop an advanced event-retrieval system. As a participant in the esteemed Ho Chi Minh AI Challenge 2024, our team, AIO_TOP10, is dedicated to harnessing the power of artificial intelligence to create a robust and efficient event-retrieval solution.

More details about the challenge refers to this link.

🎯 Features

🤖 Tech Stack

Server building

  • Back-end: FastAPI.
  • Front-end: ...
  • Deploy: Docker.

Core technology

  • Keyframe-extraction: TransNetV2, K-Means.
  • LLM: Gemini, Groq, SambaNova, etc.
  • Embedding: CLIP.

🚀 Usage

  1. Clone the repository

  2. Setup Environment

  3. Run the application

  4. API Inference

👣 Workflow

👀 Demo

🧑‍💻 Contributors

Contributors

MinLee0210

34 commits

phatv1

2 commits

phatv1/HCMC-AIC2024-test

https://aichallenge.hochiminhcity.gov.vn/

0

stars

36

commits

Jupyter Notebook

primary language

Oct 10, 2024

updated

README

HCMC-AIC2024

Joining forces with innovators and AI enthusiasts, this project is a dynamic collaboration aimed at crafting a cutting-edge event-retrieval system, proudly participating in the Ho Chi Minh AI Challenge 2024.

Table of Contents

📍 Overview

Welcome! This project is a collaborative endeavor to develop an advanced event-retrieval system. As a participant in the esteemed Ho Chi Minh AI Challenge 2024, our team, AIO_TOP10, is dedicated to harnessing the power of artificial intelligence to create a robust and efficient event-retrieval solution.

More details about the challenge refers to this link.

🎯 Features

🤖 Tech Stack

Server building

  • Back-end: FastAPI.
  • Front-end: ...
  • Deploy: Docker.

Core technology

  • Keyframe-extraction: TransNetV2, K-Means.
  • LLM: Gemini, Groq, SambaNova, etc.
  • Embedding: CLIP.

🚀 Usage

  1. Clone the repository

  2. Setup Environment

  3. Run the application

  4. API Inference

👣 Workflow

👀 Demo

🧑‍💻 Contributors

Contributors

MinLee0210

34 commits

phatv1

2 commits

Languages

Jupyter Notebook

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