Cell detection: Datasets & Models

Manual cell counting and annotation under a microscope is slow, tedious, and prone to inter-observer variability, and it does not scale when a lab processes hundreds of slides a day. Automating cell detection with a trained model gives consistent, repeatable results and frees technicians and researchers to focus on interpretation rather than tallying. A well-labeled cell dataset paired with a detection model can process images in seconds and flag samples that need a closer look. This is especially valuable when building reproducible pipelines for research or high-throughput screening.

Teams get faster, more consistent cell counts and classifications that reduce manual labor and human error. The models run on images from existing microscopes and slide scanners without new capture hardware, and they scale from a single benchtop workflow to batch processing thousands of images. Using RF-DETR, you can train a robust detector on your own cell dataset and deploy it wherever your imaging pipeline runs.

1. Upload an image: Drop any photo into the widget above to try cell detection for free, right on this page.
2. Preview instant results: The model detects red blood cell, white blood cell, platelet, cancer cell, cell nucleus 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.
3. 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 RF-DETR on your own images for production accuracy.
4. 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.