krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-29 16:08:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

Update facial-recognition.md

Browse Source
This commit is contained in:
davidmyriel
2024-12-03 16:18:22 -08:00
parent b3c6a7e79c
commit 769fa87003
1 changed files with 9 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/blog/facial-recognition.md
+9 -8
View File
@@ -38,24 +38,21 @@ We interviewed the engineer behind this project, [**Miguel Otero Pedrido**](http
<iframe width="560" height="315" src="https://www.youtube.com/embed/UJ2jTEBae3A?si=m9sHtiXTY4n0OsB2" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
Miguel recently published a video on his YouTube channel: [**The Neural Maze**](https://www.youtube.com/@TheNeuralMaze).
For detailed steps to build the app, watch [**Building a Twin Celebrity App**](https://www.youtube.com/watch?v=LltFAum3gVg).
___
## Architecture
**Search Engine:** [Qdrant](https://qdrant.tech) stands out as a high-performance [**vector database**](/qdrant-vector-database/) built in Rust, known for its reliability and speed. Its advanced features, such as [**vector visualization**](/documentation/web-ui/) and efficient [**querying**](/documentation/concepts/search/), make it a go-to choice for developers working on embedding-based projects.
**Search Engine & DB:** [**Qdrant**](https://qdrant.tech) stands out as a high-performance [**vector database**](/qdrant-vector-database/) built in Rust, known for its reliability and speed. Its advanced features, such as [**vector visualization**](/documentation/web-ui/) and efficient [**querying**](/documentation/concepts/search/), make it a go-to choice for developers working on embedding-based projects.
![architecture](/blog/facial-recognition/architecture.png)
**ML Framework:** [ZenML](https://www.zenml.io) simplifies pipeline creation with a modular, cloud-agnostic framework that ensures clean, scalable, and portable code, ideal for cross-platform workflows.
**ML Framework:** [**ZenML**](https://www.zenml.io) simplifies pipeline creation with a modular, cloud-agnostic framework that ensures clean, scalable, and portable code, ideal for cross-platform workflows.
**Facial Recognition:** [MTCNN](https://github.com/ipazc/mtcnn#) ensures consistent face alignment, making the embeddings more reliable.
**Facial Recognition:** [**MTCNN**](https://github.com/ipazc/mtcnn#) ensures consistent face alignment, making the embeddings more reliable.
**Embedding Model:** [FaceNet](https://github.com/davidsandberg/facenet) provides lightweight, pre-trained facial embeddings, balancing accuracy and efficiency, making it perfect for tasks like the Twin Celebrity app.
**Embedding Model:** [**FaceNet**](https://github.com/davidsandberg/facenet) provides lightweight, pre-trained facial embeddings, balancing accuracy and efficiency, making it perfect for tasks like the Twin Celebrity app.
**Frontend:** [Streamlit](https://github.com/streamlit) streamlines UI development, enabling rapid prototyping with minimal effort, allowing developers to focus on core functionalities.
**Frontend:** [**Streamlit**](https://github.com/streamlit) streamlines UI development, enabling rapid prototyping with minimal effort, allowing developers to focus on core functionalities.
## Application Workflows
@@ -86,6 +83,10 @@ Finally, the results are displayed in an intuitive interface, showing the user t
## How to Build the App
Miguel recently published a video on his YouTube channel: [**The Neural Maze**](https://www.youtube.com/@TheNeuralMaze).
For detailed steps to build the app, watch [**Building a Twin Celebrity App**](https://www.youtube.com/watch?v=LltFAum3gVg).
### 1. Set Up the Offline Pipeline
Using ZenML, the pipeline consists of:
- **Data Loading**: Fetch images and labels (e.g., "Brad Pitt") from Hugging Face.