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Roboflow hosts the world's biggest set of open source biology and biological datasets and pre-trained computer vision models. Captured from microscopes, handheld devices, etc. These projects can help you find objects of interest in things like Petri dishes, agar plates, museum or aquarium displays, and more. This section also features a highlighted project from Exploratorium (, a science education and R&D museum in San Francisco, California.


Background Information

This dataset was created by Michael Shamash and contains the images used to train the OnePetri plaque detection model (plaque detection model v1.0).

In microbiology, a plaque is defined as a “clear area on an otherwise opaque field of bacteria that indicates the inhibition or dissolution of the bacterial cells by some agent, either a virus or an antibiotic. Plaques are a sensitive laboratory indicator of the presence of some anti-bacterial factor.”
When working with bacteriophages (phages), viruses which can only infect and kill bacteria, scientists often need to perform the time-intensive monotonous task of counting plaques on Petri dishes. To help solve this problem I developed OnePetri, a set of machine learning models and a mobile phone application (currently iOS-only) that accelerates common microbiological Petri dish assays using AI.

A task that once took microbiologists several minutes to do per Petri dish (adds up quickly considering there are often tens of Petri dishes to analyze at a time!) could now be mostly automated thanks to computer vision, and completed in a matter of seconds.

App in Action

Video Clip

Petri Dish

Example Image

Plaque Detection

A total of 43 source images were used in this dataset with the following split: 29 training, 9 validation, 5 testing (2505 images after preprocessing and augmentations are applied).

OnePetri is a mobile phone application (currently iOS-only) which accelerates common microbiological Petri dish assays using AI. OnePetri's YOLOv5s plaque detection model was trained on a diverse set of images from the HHMI's SEA-PHAGES program, many of which are included in this dataset. This project wouldn't be possible without their support!

The following pre-processing options were applied:

  1. Auto-orient
  2. Tile image into 5 rows x 5 columns
  3. Resize tiles to 416px x 416px

The following augmentation options were applied:

  1. Grayscale (35% of images)
  2. Hue shift (-45deg to +45deg)
  3. Blur up to 2px
  4. Mosaic

OnePetri App In Action

For more information and to download OnePetri please visit:

Graduated Cylinder

  • LeNgineer
  • Graduated-Cylinder Dataset
  • 50 images

Petri Dish

Test Tube


  • explo1
  • microscope-objects Dataset
  • 34 images

Rotifers, Microbeads and Algae

By Jord Liu and The Exploratorium


This is the Machine Learning half of a larger project at the Exploratorium's Biology Lab called Seeing Scientifically, which is a research project that investigates how to use machine learning and other exhibit technology to best teach visitors in an informal learning context like the Exploratorium.

In this iteration of the project, we train an ML model to detect microscopic animals called rotifers, parts of their body (e.g. head, gut, jaw), and microbeads and algae in real time. This model is then integrated into a museum exhibit kiosk prototype that is deployed live on the Exploratorium's museum floor, and visitor research is collected on the efficacy of the exhibit.

Short gif demo of ML detection

Data and Model

The images used here are captured directly from a microscope feed and then labelled by Exploratorium employees and volunteers. Some include up to hundreds of microbeads or algae, some are brightfield and some are darkfield. They show rotifers in multiple poses, including some where the tails are not readily visible. There is relatively little variance in the images here as the environment is highly controlled. We use tiled data of multiple sizes mixed in with the full images.

We use YOLOv4, though future work includes retraining with YOLO-R, YOLO-v7, and other SOTA models. We also experimented with KeypointRCNN for pose estimation but found that the performance did not exceed our baseline of using YOLOv4 and treating the keypoints as objects.

Current performance by class is:
class_id = 1, name = bead, ap = 77.01% (TP = 251, FP = 41)
class_id = 2, name = bigbead, ap = 82.46% (TP = 36, FP = 5)
class_id = 3, name = egg, ap = 95.51% (TP = 16, FP = 4)
class_id = 4, name = gut, ap = 82.55% (TP = 70, FP = 13)
class_id = 5, name = head, ap = 78.38% (TP = 59, FP = 3)
class_id = 6, name = mastics, ap = 86.82% (TP = 49, FP = 6)
class_id = 7, name = poop, ap = 56.27% (TP = 34, FP = 15)
class_id = 8, name = rotifer, ap = 72.60% (TP = 83, FP = 17)
class_id = 9, name = tail, ap = 46.14% (TP = 27, FP = 7)


Screen captures from our exhibit as of July 2022.
Rotifer body parts
Microbead detection
Algae detection






This is a dataset of blood cells photos, originally open sourced by cosmicad and akshaylambda.

There are 364 images across three classes: WBC (white blood cells), RBC (red blood cells), and Platelets. There are 4888 labels across 3 classes (and 0 null examples).

Here's a class count from Roboflow's Dataset Health Check:

BCCD health

And here's an example image:

Blood Cell Example

Fork this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 500x500 export.

Use Cases

This is a small scale object detection dataset, commonly used to assess model performance. It's a first example of medical imaging capabilities.

Using this Dataset

We're releasing the data as public domain. Feel free to use it for any purpose.

It's not required to provide attribution, but it'd be nice! :)

About Roboflow

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