Transform Your Visual Strategy with AI
Advanced saliency prediction and eye-tracking simulations powered by deep learning.
About AdVizion
AdVizion leverages advanced artificial intelligence to evaluate and predict visual attention in digital media. Our vision is to empower advertisers, UI/UX designers, and content creators with insights that sharpen engagement.
Backed by a team of AI specialists and cognitive scientists, we deliver state-of-the-art solutions that push the boundaries of what’s possible in visual analytics—so your brand can shine.
We’d Love to Hear From You
Join Our Community
Connect with fellow creators and get instant support on Discord.
Join Our DiscordHow It Works
- Login or create an account for instant access.
- Select Media: Choose image/video or “Ad/UX Analysis.”
- Configure Options: Saliency vs. Sequence, or competitor images for ads.
- Review Results: Download or view your final output or suggestions.
- Iterate & Refine: Apply insights to improve your design’s effectiveness.
Go Premium & Accelerate Your Growth
Upgrade to unlock unlimited predictions, premium API access, advanced support, and more.
- Unlimited uploads & predictions
- Priority in the processing queue
- Exclusive features & early releases
- Premium customer support
AdVizion API
Power up your own applications with AdVizion’s Saliency & Eye-Tracking predictions. Our API enables you to upload images or videos and retrieve analysis results with ease.
Integration Overview
Include your API key in the request headers as follows:
x-api-key: YOUR_API_KEYEndpoints
- POST /api/upload/: Submits an image and returns the processed saliency map.
- POST /api/video/: Submits a video for frame-by-frame analysis and returns the processed video URL.
Parameters
prediction_type: saliency or sequence. Defaults to saliency.
fixation_points: For sequence, specify [1..7].
condition: For saliency, choose [0=Natural,1=E-Commercial,2=UI].
interval: For video processing, a float (seconds) that sets how frequently frames are analyzed.
Code Examples
import requests
api_key = "YOUR_API_KEY"
headers = {"x-api-key": api_key}
# Sequence prediction
with open("path_to_your_image.jpg", "rb") as f:
files = {"file": f}
data = {"prediction_type": "sequence", "fixation_points": 3}
response = requests.post("https://www.advizion.net/api/upload/",
headers=headers, files=files, data=data)
print(response.json())
# Saliency with condition
with open("path_to_your_image.jpg", "rb") as f:
files = {"file": f}
data = {"prediction_type": "saliency", "condition": 2}
response = requests.post("https://www.advizion.net/api/upload/",
headers=headers, files=files, data=data)
print(response.json())
# Video upload
with open("path_to_your_video.mp4", "rb") as f:
files = {"file": f}
data = {"interval": 0.5, "condition": 1}
response = requests.post("https://www.advizion.net/api/video/",
headers=headers, files=files, data=data)
print(response.json())
// Using Axios in TypeScript
import axios from 'axios';
import * as fs from 'fs';
const apiKey = 'YOUR_API_KEY';
const headers = {
'x-api-key': apiKey,
'Content-Type': 'multipart/form-data'
};
// Image upload (sequence)
const imageFile = fs.createReadStream('path_to_your_image.jpg');
const imageForm = new FormData();
imageForm.append('file', imageFile);
imageForm.append('prediction_type', 'sequence');
imageForm.append('fixation_points', '3');
axios.post('https://www.advizion.net/api/upload/', imageForm, { headers })
.then(res => console.log(res.data))
.catch(err => console.error(err));
// Image upload (saliency)
const salImageFile = fs.createReadStream('path_to_your_image.jpg');
const salForm = new FormData();
salForm.append('file', salImageFile);
salForm.append('prediction_type', 'saliency');
salForm.append('condition', '2');
axios.post('https://www.advizion.net/api/upload/', salForm, { headers })
.then(res => console.log(res.data))
.catch(err => console.error(err));
// Video upload
const videoFile = fs.createReadStream('path_to_your_video.mp4');
const videoForm = new FormData();
videoForm.append('file', videoFile);
videoForm.append('interval', '0.5');
videoForm.append('condition', '1');
axios.post('https://www.advizion.net/api/video/', videoForm, { headers })
.then(res => console.log(res.data))
.catch(err => console.error(err));
# Image Upload (Sequence)
curl -X POST https://www.advizion.net/api/upload/ \
-H "x-api-key: YOUR_API_KEY" \
-F "file=@/path/to/your/image.jpg" \
-F "prediction_type=sequence" \
-F "fixation_points=3"
# Image Upload (Saliency)
curl -X POST https://www.advizion.net/api/upload/ \
-H "x-api-key: YOUR_API_KEY" \
-F "file=@/path/to/your/image.jpg" \
-F "prediction_type=saliency" \
-F "condition=2"
# Video Upload
curl -X POST https://www.advizion.net/api/video/ \
-H "x-api-key: YOUR_API_KEY" \
-F "file=@/path/to/your/video.mp4" \
-F "interval=0.5" \
-F "condition=1"