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) - 496 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) - 1,190 images

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416), all classes remapped (Modify Classes) to "pill"

• Augmentations:
  Outputs per training example: 3
  90° Rotate: Clockwise, Counter-Clockwise, Upside Down
  Shear: ±5° Horizontal, ±5° Vertical
  Hue: Between -25° and +25°
  Saturation: Between -10% and +10%
  Brightness: Between -10% and +10%
  Exposure: Between -10% and +10%
  Noise: Up to 2% of pixels
  Cutout: 5 boxes with 5% size each

• Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow

NOTE:

The Isolate Objects preprocessing step was added to convert the original 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 in this project.

Mohamed Attia - LinkedIn

Garbage Object-Detection to Identify Disposal Class

This dataset detects various kinds of waste, labeling with a class that indentifies how it should be disposed

About this Dataset

This dataset was created by exporting images from images.cv and labeling them as an object detection dataset. The dataset contains 421 raw images (v1 - raw-images) and labeled classes include:

• forklift
• person

Recyclable Items Dataset and Model

This is an object detection dataset that contains the classes below:

• Plastic
• Glass
• Metal

Usage

This model can be potentially used to detect the objects above in an effort to sort them in a recycling center or an Automated River Cleaning System that uses computer vision.

Overview

The Hard Hat dataset is an object detection dataset of workers in workplace settings that require a hard hat. Annotations also include examples of just "person" and "head," for when an individual may be present without a hard hart.

Example Image:

Use Cases

One could use this dataset to, for example, build a classifier of workers that are abiding safety code within a workplace versus those that may not be. It is also a good general dataset for practice.

Using this Dataset

Use the fork button to copy this dataset to your own Roboflow account and export it with new preprocessing settings (perhaps resized for your model's desired format or converted to grayscale), or additional augmentations to make your model generalize better. This particular dataset would be very well suited for Roboflow's new advanced Bounding Box Only Augmentations.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.

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

This project is trying to create an efficient computer or machine vision model to detect different kinds of construction equipment in construction sites and we are starting with three classes which are excavators, trucks, and wheel loaders.

The dataset is provided by Mohamed Sabek, a Spring 2022 Master of Science graduate from Arizona State University in Construction Management and Technology.

The raw images (v1) contains:

1. 1,532 annotated examples of "excavators"
2. 1,269 annotated examples of "dump truck"
3. 1,080 annotated examples of "wheel loader"

Note: versions 2 and 3 (v2 and v3) contain the raw images resized at 416 by 416 (stretch to) and 640 by 640 (stretch to) without any augmentations.

Personal Protective Equipment Dataset and Model

This dataset is a collection of images that contains annotations for the classes below:

• goggles
• helmet
• mask
• no-suit
• no_goggles
• no_helmet
• no_mask
• no_shoes
• shoes
• suit
• no_glove
• glove

Usage

Most of these classes are underrepresented and would need to be balanced for better detection. An improved model can be utilized for use cases that'll detect the classes above in order to minimize exposure to hazards that cause serious workplace injuries.