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<title>AI Video Action Recognition | TimeSformer</title>
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</head>
<body>
<!-- Header -->
<header>
<nav class="container">
<a href="#" class="logo">
<i class="fas fa-video"></i>
VideoAI
</a>
<ul class="nav-links">
<li><a href="#deployment">How to Use</a></li>
<li><a href="#features">Features</a></li>
<li><a href="#installation">Setup</a></li>
<li>
<a
href="https://github.com/u-justine/VideoActionRecognition"
target="_blank"
>
<i class="fab fa-github"></i> GitHub
</a>
</li>
</ul>
</nav>
</header>
<!-- Hero Section -->
<section class="hero">
<div class="particles">
<!-- Animated particles will be added via JavaScript -->
</div>
<div class="container hero-content">
<h1 class="fade-in-up">AI Video Action Recognition</h1>
<p class="fade-in-up">
Powered by Facebook's TimeSformer model, this application
can identify and classify human actions in video clips with
state-of-the-art accuracy.
</p>
<div class="cta-buttons fade-in-up">
<a href="#deployment" class="btn btn-primary">
<i class="fas fa-play"></i>
Get Started
</a>
<a
href="https://github.com/U-justine/VideoActionRecognition"
class="btn btn-secondary"
target="_blank"
>
<i class="fab fa-github"></i>
View Source
</a>
</div>
</div>
</section>
<!-- Notice Section -->
<section class="notice">
<div class="container">
<div class="notice-content">
<h3>
<i class="fas fa-info-circle"></i> How to Access the
Live Demo
</h3>
<p>
<strong
>This GitHub Pages site shows the project
information.</strong
>
To actually upload videos and test the AI model, you
need to run the application locally or deploy it to a
cloud platform.
</p>
<p>
Choose one of the deployment options below to start
using the video action recognition feature!
</p>
</div>
</div>
</section>
<!-- Deployment Options -->
<section id="deployment" class="deployment">
<div class="container">
<h2>How to Use the App</h2>
<div class="deployment-grid">
<div class="deployment-card">
<div class="deployment-icon" style="color: #4ade80">
<i class="fas fa-desktop"></i>
</div>
<h3>Run Locally</h3>
<p>
Download and run the application on your computer.
This gives you full control and doesn't require any
cloud credits.
</p>
<a href="#installation" class="btn btn-primary">
<i class="fas fa-download"></i>
Setup Guide
</a>
</div>
<div class="deployment-card">
<div class="deployment-icon" style="color: #3b82f6">
<i class="fab fa-google"></i>
</div>
<h3>Google Colab</h3>
<p>
Run the app in Google Colab with GPU acceleration.
Perfect for quick testing without local
installation.
</p>
<a
href="https://colab.research.google.com/github/u-justine/VideoActionRecognition/blob/main/VideoActionRecognition_Colab.ipynb"
class="btn btn-primary"
target="_blank"
>
<i class="fas fa-external-link-alt"></i>
Open Colab
</a>
</div>
<div class="deployment-card">
<div class="deployment-icon" style="color: #8b5cf6">
<i class="fas fa-cloud"></i>
</div>
<h3>Hugging Face Spaces</h3>
<p>
Try the live demo hosted on Hugging Face Spaces.
Upload your video directly in the browser.
</p>
<a
href="https://huggingface.co/spaces/u-justine/video-action-recognition"
class="btn btn-primary"
target="_blank"
>
<i class="fas fa-rocket"></i>
Live Demo
</a>
</div>
</div>
</div>
</section>
<!-- Features Section -->
<section id="features" class="features">
<div class="container">
<h2>Key Features</h2>
<div class="features-grid">
<div class="feature-card">
<div class="feature-icon">
<i class="fas fa-brain"></i>
</div>
<h3>AI-Powered Recognition</h3>
<p>
Uses Facebook's TimeSformer model fine-tuned on
Kinetics-400 dataset with 400+ action classes for
accurate predictions.
</p>
</div>
<div class="feature-card">
<div class="feature-icon">
<i class="fas fa-bolt"></i>
</div>
<h3>Real-Time Processing</h3>
<p>
Efficiently processes videos using GPU acceleration
when available, with fallback to CPU for universal
compatibility.
</p>
</div>
<div class="feature-card">
<div class="feature-icon">
<i class="fas fa-upload"></i>
</div>
<h3>Easy Upload</h3>
<p>
Simple drag-and-drop interface supporting multiple
video formats (MP4, MOV, AVI, MKV) up to 200MB.
</p>
</div>
<div class="feature-card">
<div class="feature-icon">
<i class="fas fa-chart-bar"></i>
</div>
<h3>Detailed Results</h3>
<p>
Get top-k predictions with confidence scores and
visual feedback for better understanding of model
decisions.
</p>
</div>
<div class="feature-card">
<div class="feature-icon">
<i class="fas fa-list"></i>
</div>
<h3>400+ Actions</h3>
<p>
Recognizes sports, daily activities, musical
performances, exercise, work activities, and social
interactions.
</p>
</div>
<div class="feature-card">
<div class="feature-icon">
<i class="fab fa-osi"></i>
</div>
<h3>Open Source</h3>
<p>
Complete source code available on GitHub with
detailed documentation and setup instructions.
</p>
</div>
</div>
</div>
</section>
<!-- Installation Section -->
<section id="installation" class="installation">
<div class="container">
<h2>Local Installation</h2>
<div style="max-width: 800px; margin: 0 auto">
<h3 style="margin-bottom: 1rem">1. Clone the Repository</h3>
<div class="code-block">
git clone
https://github.com/u-justine/VideoActionRecognition.git
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<h3 style="margin: 2rem 0 1rem 0">2. Setup Environment</h3>
<div class="code-block">
cd VideoActionRecognition && python3 -m venv .venv &&
source .venv/bin/activate
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<i class="fas fa-copy"></i>
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<h3 style="margin: 2rem 0 1rem 0">
3. Install Dependencies
</h3>
<div class="code-block">
pip install -r requirements.txt
<button
class="copy-btn"
onclick="copyToClipboard('pip install -r requirements.txt')"
>
<i class="fas fa-copy"></i>
</button>
</div>
<h3 style="margin: 2rem 0 1rem 0">
4. Run the Application
</h3>
<div class="code-block">
./run_app.sh
<button
class="copy-btn"
onclick="copyToClipboard('./run_app.sh')"
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<i class="fas fa-copy"></i>
</button>
</div>
<div
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border: 1px solid #38b2ac;
border-radius: 10px;
padding: 1.5rem;
margin-top: 2rem;
"
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<div
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align-items: center;
gap: 0.5rem;
margin-bottom: 0.5rem;
"
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<i
class="fas fa-info-circle"
style="color: #38b2ac"
></i>
<strong style="color: #234e52">Pro Tips</strong>
</div>
<ul
style="
color: #234e52;
margin: 0;
padding-left: 1rem;
"
>
<li>
If dependencies fail to install, run
<code>./run_fix.sh</code> first
</li>
<li>
The app will open at
<code>http://localhost:8501</code> in your
browser
</li>
<li>
Use GPU-enabled environment for faster
processing
</li>
</ul>
</div>
</div>
</div>
</section>
<!-- Footer -->
<footer>
<div class="container">
<div class="footer-content">
<div>
<p>
© 2024 Video Action Recognition. Built with ❤️
using TimeSformer.
</p>
</div>
<div class="social-links">
<a
href="https://github.com/u-justine/VideoActionRecognition"
target="_blank"
title="GitHub Repository"
>
<i class="fab fa-github"></i>
</a>
<a
href="https://huggingface.co/facebook/timesformer-base-finetuned-k400"
target="_blank"
title="TimeSformer Model"
>
<i class="fas fa-robot"></i>
</a>
<a
href="https://arxiv.org/abs/2102.05095"
target="_blank"
title="Research Paper"
>
<i class="fas fa-file-alt"></i>
</a>
</div>
</div>
</div>
</footer>
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entries.forEach((entry) => {
if (entry.isIntersecting) {
entry.target.style.opacity = "1";
entry.target.style.transform = "translateY(0)";
}
});
}, observerOptions);
// Observe elements for scroll animations
document.addEventListener("DOMContentLoaded", () => {
const elements = document.querySelectorAll(
".feature-card, .deployment-card",
);
elements.forEach((el) => {
el.style.opacity = "0";
el.style.transform = "translateY(30px)";
el.style.transition =
"opacity 0.6s ease, transform 0.6s ease";
observer.observe(el);
});
});
</script>
</body>
</html>
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