docs/en/datasets/detect/medical-pills.md
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb"></a>
The Ultralytics Medical Pills dataset is a proof-of-concept (POC) object detection dataset of 115 labeled images across a single class, pill — 92 for training and 23 for validation. It is built to demonstrate computer vision models for pharmaceutical applications such as quality control, packaging automation, and sorting.
<strong>Watch:</strong> How to train an Ultralytics YOLO Model on the Medical Pills Detection Dataset in <a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb">Google Colab</a>
</p>The Medical Pills dataset contains 115 images annotated with a single class, pill, split into two subsets defined by the medical-pills.yaml configuration:
| Split | Images | Description |
|---|---|---|
| Train | 92 | Labeled images for model training |
| Validation | 23 | Held-out images for evaluation and benchmarking |
Explore Medical Pills on Ultralytics Platform to browse the images with their annotation overlays, view the class distribution and bounding-box heatmaps in the Charts tab, and clone it to train your own model in the cloud.
Using computer vision for medical pills detection enables automation in the pharmaceutical industry, supporting tasks like:
The medical-pills.yaml file defines the dataset configuration — the dataset paths, class names, and other metadata. It is maintained in the Ultralytics repository at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml.
!!! example "ultralytics/cfg/datasets/medical-pills.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/medical-pills.yaml"
```
To train a YOLO26n model on the Medical Pills dataset for 100 epochs with an image size of 640, use the following examples. For detailed arguments, refer to the model's Training page.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="medical-pills.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=medical-pills.yaml model=yolo26n.pt epochs=100 imgsz=640
```
!!! example "Inference Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("path/to/best.pt") # load a fine-tuned model
# Inference using the model
results = model.predict("https://ultralytics.com/assets/medical-pills-sample.jpg")
```
=== "CLI"
```bash
# Start prediction with a fine-tuned *.pt model
yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/medical-pills-sample.jpg"
```
The Medical Pills dataset features labeled images showcasing the diversity of pills. Below is an example of a labeled image from the dataset:
For more comprehensive pharmaceutical analysis, consider combining the Medical Pills dataset with other related datasets like package-seg for packaging identification or medical imaging datasets like brain-tumor to develop end-to-end healthcare AI solutions.
The dataset is available under the AGPL-3.0 License.
If you use the Medical Pills dataset in your research or development work, please cite it using the mentioned details:
!!! quote ""
=== "BibTeX"
```bibtex
@dataset{Jocher_Ultralytics_Datasets_2024,
author = {Jocher, Glenn and Rizwan, Muhammad},
license = {AGPL-3.0},
month = {Dec},
title = {Ultralytics Datasets: Medical-pills Detection Dataset},
url = {https://docs.ultralytics.com/datasets/detect/medical-pills/},
version = {1.0.0},
year = {2024}
}
```
The Medical Pills dataset contains 115 images total — 92 for training and 23 for validation — with no separate test split. Each image is annotated with a single class, pill. It ships as an 8.19 MB automatic download defined in the medical-pills.yaml configuration.
You can train a YOLO26 model for 100 epochs with an image size of 640px using the Python or CLI methods provided. Refer to the Training Example section for detailed instructions and check the YOLO26 documentation for more information on model capabilities.
The dataset enables automation in pill detection, contributing to counterfeit prevention, quality assurance, and pharmaceutical process optimization. It also serves as a valuable resource for developing AI solutions that can improve medication safety and supply chain efficiency.
Inference can be done using Python or CLI methods with a fine-tuned YOLO26 model. Refer to the Inference Example section for code snippets and the Predict mode documentation for additional options.
The YAML file is available at medical-pills.yaml, containing dataset paths, classes, and additional configuration details essential for training models on this dataset.