154 lines
8.1 KiB
Markdown
154 lines
8.1 KiB
Markdown
---
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comments: true
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description: Explore the medical-pills detection dataset with labeled images. Essential for training AI models for pharmaceutical identification and automation.
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keywords: medical-pills dataset, pill detection, pharmaceutical imaging, AI in healthcare, computer vision, object detection, medical automation, dataset for training
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---
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# Medical Pills Dataset
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<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Medical Pills Dataset In Colab"></a>
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The medical-pills detection dataset is a proof-of-concept (POC) dataset, carefully curated to demonstrate the potential of AI in pharmaceutical applications. It contains labeled images specifically designed to train [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) [models](https://docs.ultralytics.com/models/) for identifying medical-pills.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/8gePl_Zcs5c"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> How to train Ultralytics YOLO11 Model on 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>
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</p>
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This dataset serves as a foundational resource for automating essential [tasks](https://docs.ultralytics.com/tasks/) such as quality control, packaging automation, and efficient sorting in pharmaceutical workflows. By integrating this dataset into projects, researchers and developers can explore innovative [solutions](https://docs.ultralytics.com/solutions/) that enhance [accuracy](https://www.ultralytics.com/glossary/accuracy), streamline operations, and ultimately contribute to improved healthcare outcomes.
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## Dataset Structure
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The medical-pills dataset is divided into two subsets:
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- **Training set**: Consisting of 92 images, each annotated with the class `pill`.
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- **Validation set**: Comprising 23 images with corresponding annotations.
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## Applications
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Using computer vision for medical-pills detection enables automation in the pharmaceutical industry, supporting tasks like:
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- **Pharmaceutical Sorting**: Automating the sorting of pills based on size, shape, or color to enhance production efficiency.
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- **AI Research and Development**: Serving as a benchmark for developing and testing computer vision algorithms in pharmaceutical use cases.
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- **Digital Inventory Systems**: Powering smart inventory solutions by integrating automated pill recognition for real-time stock monitoring and replenishment planning.
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- **Quality Control**: Ensuring consistency in pill production by identifying defects, irregularities, or contamination.
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- **Counterfeit Detection**: Helping identify potentially counterfeit medications by analyzing visual characteristics against known standards.
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## Dataset YAML
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A YAML configuration file is provided to define the dataset's structure, including paths and classes. For the medical-pills dataset, the `medical-pills.yaml` file can be accessed at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml).
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!!! example "ultralytics/cfg/datasets/medical-pills.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/medical-pills.yaml"
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```
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## Usage
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To train a YOLO11n model on the medical-pills dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following examples. For detailed arguments, refer to the model's [Training](../../modes/train.md) page.
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="medical-pills.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo detect train data=medical-pills.yaml model=yolo11n.pt epochs=100 imgsz=640
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```
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!!! example "Inference Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("path/to/best.pt") # load a fine-tuned model
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# Inference using the model
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results = model.predict("https://ultralytics.com/assets/medical-pills-sample.jpg")
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```
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=== "CLI"
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```bash
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# Start prediction with a fine-tuned *.pt model
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yolo detect predict model='path/to/best.pt' imgsz=640 source="https://ultralytics.com/assets/medical-pills-sample.jpg"
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```
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## Sample Images and Annotations
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The medical-pills dataset features labeled images showcasing the diversity of pills. Below is an example of a labeled image from the dataset:
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- **Mosaiced Image**: Displayed is a training batch comprising mosaiced dataset images. Mosaicing enhances training diversity by consolidating multiple images into one, improving model generalization.
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## Integration with Other Datasets
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For more comprehensive pharmaceutical analysis, consider combining the medical-pills dataset with other related datasets like [package-seg](../segment/package-seg.md) for packaging identification or medical imaging datasets like [brain-tumor](brain-tumor.md) to develop end-to-end healthcare AI solutions.
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## Citations and Acknowledgments
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The dataset is available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
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If you use the Medical-pills dataset in your research or development work, please cite it using the mentioned details:
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!!! quote ""
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=== "BibTeX"
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```bibtex
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@dataset{Jocher_Ultralytics_Datasets_2024,
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author = {Jocher, Glenn and Rizwan, Muhammad},
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license = {AGPL-3.0},
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month = {Dec},
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title = {Ultralytics Datasets: Medical-pills Detection Dataset},
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url = {https://docs.ultralytics.com/datasets/detect/medical-pills/},
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version = {1.0.0},
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year = {2024}
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}
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```
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## FAQ
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### What is the structure of the medical-pills dataset?
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The dataset includes 92 images for training and 23 images for validation. Each image is annotated with the class `pill`, enabling effective training and evaluation of models for pharmaceutical applications.
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### How can I train a YOLO11 model on the medical-pills dataset?
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You can train a YOLO11 model for 100 epochs with an image size of 640px using the Python or CLI methods provided. Refer to the [Training Example](#usage) section for detailed instructions and check the [YOLO11 documentation](../../models/yolo11.md) for more information on model capabilities.
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### What are the benefits of using the medical-pills dataset in AI projects?
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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.
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### How do I perform inference on the medical-pills dataset?
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Inference can be done using Python or CLI methods with a fine-tuned YOLO11 model. Refer to the [Inference Example](#usage) section for code snippets and the [Predict mode documentation](../../modes/predict.md) for additional options.
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### Where can I find the YAML configuration file for the medical-pills dataset?
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The YAML file is available at [medical-pills.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/medical-pills.yaml), containing dataset paths, classes, and additional configuration details essential for training models on this dataset.
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