---
title: Peak-End-Net Video Aesthetic Assessment
emoji: 🎬
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 6.20.0
app_file: app.py
short_description: Video aesthetic scoring via peak-end rule (11 scores)
python_version: "3.12"
startup_duration_timeout: 30m
---

# Peak-End-Net: Video Aesthetic Assessment

Upload a video to get an **overall aesthetic score** and **ten fine-grained attribute scores**
based on the *peak-end rule* from cognitive psychology.

## How it works

Peak-End-Net evaluates video aesthetics by focusing on the most salient ("peak") moments
and the ending of a video, rather than averaging all frames equally. It uses a frozen CLIP
ViT-L/14 backbone with an AVA-pretrained aesthetic head, a key-moment discovery module,
peak-end aggregation, a rhythm encoder, and a gated fusion module.

## Scores

| Score | Description |
|---|---|
| **overall** | Overall aesthetic quality (gated fusion) |
| **composition** | Visual composition and framing |
| **shotsize** | Shot size / framing distance |
| **lighting** | Lighting quality and mood |
| **visualtone** | Visual tone and atmosphere |
| **color** | Color palette and grading |
| **depthoffield** | Depth of field and focus |
| **expression** | Emotional expression (human subjects) |
| **movement** | Camera and subject movement |
| **costume** | Costume and styling |
| **makeup** | Makeup quality |

## Model

- **Model:** [GD-ML/Peak-End-Net](https://huggingface.co/GD-ML/Peak-End-Net)
- **Paper:** [arXiv:2607.13941](https://arxiv.org/abs/2607.13941)
- **Code:** [GitHub](https://github.com/AMAP-ML/Peak-End-Net)