Cell counting: Datasets & Models
Automated counting of cells in microscopy images using object detection. Locates and tallies individual cells across blood smears, cultures, and tissue samples.
Cell counting
This computer vision workflow uses object detection to locate and count individual cells in microscopy imagery, placing a bounding box around each cell so it can be tallied automatically. Trained on a cell counting dataset, it accepts inputs such as brightfield and fluorescence microscope images, hemocytometer captures, blood smears, and cultured cell plates.
Try it out
Powered by general detection model
Run on custom image
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Why automate cell counting
Manual cell counting under a microscope is slow, tedious, and prone to inter-operator variability, and technicians counting thousands of cells across many fields introduce fatigue-driven errors that are hard to reproduce. Automating cell counting with a detection model in healthcare imaging pipelines delivers consistent, repeatable tallies in seconds and frees skilled staff for analysis rather than clicking. Because each cell is individually localized, you get a verifiable count that a reviewer can spot-check against the original image. This is especially valuable for high-throughput assays where counting volume overwhelms manual workflows.
Key benefits
Detection-based counting produces per-cell localization for auditable results, scales to large image batches without added labor, and stays consistent across operators and shifts. A curated cell counting dataset paired with RF-DETR lets you fine-tune to your own stains, magnifications, and cell types, and the model integrates with existing microscope cameras and slide scanners through standard image inputs. As sample volume grows, throughput scales without adding headcount.
How to use it
- Upload an image: Drop any photo into the widget above to try cell counting for free, right on this page.
- Preview instant results: The model detects cell, red blood cell, white blood cell, cell nucleus, cell colony 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 RF-DETR 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 cell counting
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for cell counting, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Cell Counting project to Roboflow Agent.