Vehicle classification: Datasets & Models
A computer vision workflow that classifies vehicles by type from images or video frames. It assigns each detected vehicle a category label such as car, truck, bus, or motorcycle.
Vehicle classification
This vehicle classification workflow processes still images or video frames from sources like traffic cameras, tolls, and parking entrances, then assigns each vehicle a type label such as car, truck, bus, or motorcycle. It performs vehicle type detection across mixed traffic scenes, identifying what kind of vehicle is present rather than only where it is located.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate vehicle classification
Manually reviewing footage to tag vehicle types is slow, inconsistent, and impossible to scale across dozens of camera feeds or high-volume traffic corridors. Automating vehicle type detection with computer vision gives you consistent labels around the clock, freeing staff from tedious tagging and enabling data-driven decisions on tolling tiers, lane usage, parking allocation, and traffic planning. For automotive and transportation teams, this means turning raw camera streams into structured counts and category breakdowns without adding headcount. The classification runs the same way on every frame, removing the subjective variation you get from human reviewers.
Key benefits
You get reliable, repeatable vehicle categorization that plugs into cameras and infrastructure you already have, avoiding costly hardware replacement. A ViT-based classification model captures fine-grained visual differences between vehicle types and scales from a single intersection to a fleet of camera feeds. The result is faster processing, structured data you can act on, and a workflow you can retrain as new vehicle categories or camera conditions emerge.
How to use it
- Upload an image: Drop any photo into the widget above to try vehicle classification for free, right on this page.
- Preview instant results: The model detects car, truck, bus, motorcycle, van within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train Classification (ViT) on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for vehicle classification
When you're ready to build a production model for vehicle classification, we recommend training Classification (ViT). Unlike the ready-to-use model in the demo above, you fine-tune it on your own labeled images for higher accuracy, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Vehicle Classification project to Roboflow Agent.