Pill Detection Computer Vision Project

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.

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

                        @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 2024-12-22 },
                            }