Platform · ML Vision
Upload an image. Let it see.
Classify, detect, segment.
ML Vision is a computer-vision world inside AIRaML — three tasks in one app. Classify an image across 1000 categories (and compare four backbones), detect and box every object, or segment the scene pixel by pixel — all on real ONNX models from one FastAPI backend.
You upload — cat.jpg
ResNet50 predicts
An illustrative classification. Your real image gets ranked labels, detection boxes or a pixel-level segmentation overlay — your choice of task.
One app, three vision tasks
Classify, detect, segment
The three workhorse computer-vision tasks, all on real ONNX models from one backend. Each is live, and each states the models and label set behind it.
Classification
Upload an image and get the top predicted labels with confidence, from any of four ImageNet-trained backbones you can compare side by side.
Detection
Find and box every object in the image, each with its label and confidence — people, vehicles, animals, everyday objects.
Segmentation
Label the scene pixel by pixel — sky, road, building, person, furniture — as a coloured overlay on your image.
How it works
Pick → upload → read
Pick a task
Choose Classification (what is this?), Detection (what's where?) or Segmentation (which pixels are what?).
Upload an image
Drop in an image or use a sample. It's resized to each model's input size automatically. For classification, pick a backbone — or run several to compare.
Read the result
Ranked labels with confidence (classify), boxes over the image (detect), or a coloured pixel overlay with a class legend (segment).
Honest scope
Real models, with the caveats stated
Three real vision tasks on real ONNX models — compact models on fixed label sets, built to show the shape of practical vision, not to be a specialised system.
LiveWhat it does
- ✓Classification across 1000 ImageNet classes
- ✓Four backbones to compare — MobileNetV2, ResNet50, SqueezeNet, GoogLeNet
- ✓Object detection over 80 COCO classes (TinyYOLOv3)
- ✓Pixel-level segmentation over 150 ADE20K classes (SegFormer-B0)
- ✓Real ONNX models on a FastAPI backend
- ✓One microservice shared with ML Unified + EDA
CaveatsWhat to keep in mind
- —Fixed label sets — only names things in ImageNet / COCO / ADE20K
- —Compact models tuned for speed on CPU, not maximum accuracy
- —Expect misses on small, crowded or unusual scenes
- —General-purpose — not medical, security or inspection grade
- —Uploaded images are processed for the result, not kept
See your first image.
Pick a task, upload an image, and read the result — no setup, free.
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