AI-POWERED · IMAGE CAPTIONING

Every photograph,intelligently described.

Upload any image. A pre-trained vision model reads the scene, an attention-based decoder writes the caption underneath — accurate, automatic, and instant.

Cat in high contrast
Vintage camera on desk
Golden retriever puppy
"A golden retriever running across a grassy field" — 92% confidence
AI WORKSPACE

Caption Generator

Configure model settings, upload your images, and get AI-generated captions with attention visualization in real time.

READY
BATCH: 001 | COMPLETED: 0/0

MODEL SETTINGS

UPLOAD IMAGE

Drag & drop photos, click to browse, or paste from clipboard

JPG, PNG, WEBP — UP TO 10MB

IMAGE PREVIEW

NO IMAGE SELECTED

GENERATED CAPTION

0.00 SCORE
No caption generated yet. Upload a photo and click "Generate Caption".

ALTERNATIVE CAPTIONS

No alternatives generated.
IMAGE QUEUE
NO IMAGES IN QUEUE. DRAG IN PHOTOS OR CLICK UPLOAD TO GET STARTED.
HOW IT WORKS

Three steps to a caption

Upload, analyze, generate — simple as that.

STEP 01

Extract visual features

A pre-trained ResNet50 or VGG16 backbone extracts spatial features from your image — no training data needed from you.

STEP 02

Generate the caption

An attention-based decoder focuses on relevant image regions for each word, building a natural sentence one token at a time.

STEP 03

Review & refine

Compare ranked caption candidates, edit any of them inline, visualize attention maps, and export your results as CSV or JSON.

API DOCUMENTATION

Integrate it into your pipeline

One POST request, ranked captions back. Connect to a CMS, a media library, or your own batch processing workflow.

  • ✓ Top-3 ranked captions with confidence scores
  • ✓ Adjustable beam width and caption length
  • ✓ Word-level attention maps included
  • ✓ Scene type & image analysis metadata
  • ✓ 60 requests / minute on the free tier
ENDPOINTPOST /api/caption
// POST /api/caption
const res = await fetch("/api/caption", {
  method: "POST",
  headers: { 
    "Authorization": "Bearer YOUR_KEY",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    image_base64: "data:image/jpeg;base64,...",
    beam_width: 5,
    num_captions: 3
  })
});

const data = await res.json();
// data.captions = [
//   { text: "A warm sunset...", score: 0.93 },
//   { text: "A golden landscape...", score: 0.84 }
// ]
// data.analysis = { scene_type, dominant_tone, lighting }