Pill Detection Object Detection Dataset and Pre-Trained Model by Mohamed Attia

Images

451 images

Explore Dataset

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

Preprocessing: Auto-Orient, all classes remapped (Modify Classes) to "pill", Isolate Objects
- The Isolate Objects preprocessing step was added to convert this object detection project into a suitable format for export in OpenAI's CLIP annotation format so that it could be used as a classifcation model (classification dataset available here: https://universe.roboflow.com/mohamed-attia-e2mor/pill-classification)

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 2024-04-24 },
                            }