Mohamed Attia

Pill Detection

Object Detection

4

Pill Detection Computer Vision Project

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Images

451 images
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Background Information

This dataset was curated and annotated by Mohamed Attia.

The original dataset (v1) is composed of 451 images of various pills that are present on a large variety of surfaces and objects. Example of an Annotated Image from the Dataset

The dataset is available under the Public License.

Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset Versions

Version 1 (v1) - 451 images

  • Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
  • Augmentations: No augmentations applied
  • Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 1,083 images

  • Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
  • Augmentations: 90° Rotate: Clockwise, Counter-Clockwise, Upside Down Crop: 0% Minimum Zoom, 77% Maximum Zoom Rotation: Between -45° and +45° Shear: ±15° Horizontal, ±15° Vertical Hue: Between -22° and +22° Saturation: Between -27% and +27% Brightness: Between -33% and +33% Exposure: Between -25% and +25% Blur: Up to 3px Noise: Up to 5% of pixels Cutout: 3 boxes with 10% size each Mosaic: Applied Bounding Box: Brightness: Between -25% and +25%
  • Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
    • mAP = 91.4%, precision = 61.1%, recall = 93.9%

Version 3 (v3) - 1,083 images

  • Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
  • Augmentations: 90° Rotate: Clockwise, Counter-Clockwise, Upside Down Crop: 0% Minimum Zoom, 77% Maximum Zoom Rotation: Between -45° and +45° Shear: ±15° Horizontal, ±15° Vertical Hue: Between -22° and +22° Saturation: Between -27% and +27% Brightness: Between -33% and +33% Exposure: Between -25% and +25% Blur: Up to 3px Noise: Up to 5% of pixels Cutout: 3 boxes with 10% size each Mosaic: Applied Bounding Box: Brightness: Between -25% and +25%
  • Training Metrics: Trained from "scratch" (no transfer learning employed) on Roboflow
    • mAP = 84.3%, precision = 53.2%, recall = 86.7%

Version 4 (v4) - 451 images

  • Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"
  • Augmentations: No augmentations applied
  • Training Metrics: This version of the dataset was not trained

Version 5 (v5) - 496 images

Mohamed Attia - LinkedIn

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            pill-detection-llp4r_dataset,
                            title = { Pill Detection Dataset },
                            type = { Open Source Dataset },
                            author = { Mohamed Attia },
                            howpublished = { \url{ https://universe.roboflow.com/mohamed-attia-e2mor/pill-detection-llp4r } },
                            url = { https://universe.roboflow.com/mohamed-attia-e2mor/pill-detection-llp4r },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jul },
                            note = { visited on 2024-04-17 },
                            }
                        

Connect Your Model With Program Logic

Find utilities and guides to help you start using the Pill Detection project in your project.

Source

Mohamed Attia

Last Updated

2 years ago

Project Type

Object Detection

Subject

pills

Views: 9508

Views in previous 30 days: 550

Downloads: 348

Downloads in previous 30 days: 17

License

Public Domain

Classes

Cipro 500 Ibuphil 600 mg Ibuphil Cold 400-60 Xyzall 5mg blue pink red white