Browse

Classification

Datasets, Pre-Trained Models, and APIs for Object Detection, Classification

Pill Classification

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 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) - 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

Flowers

Overview

The Flowers dataset is a classification detection dataset various flower species like dandelions and daisies.

Example Image:
Example Image

Use Cases

Build a flower classifier model! Consider deploying that to a mobile app for outdoor enthusiasts or florist hobbyists.

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.

Roboflow Workmark

Rock Paper Scissors

Overview

Via Laurence Maroney:

Rock Paper Scissors contains images from a variety of different hands, from different races, ages and genders, posed into Rock / Paper or Scissors and labelled as such. You can download the training set here, and the test set here. These images have all been generated using CGI techniques as an experiment in determining if a CGI-based dataset can be used for classification against real images. I also generated a few images that you can use for predictions. You can find them here.

Note that all of this data is posed against a white background.

Each image is 300×300 pixels in 24-bit color.

There are 2520 examples examples in the trianing set, 840 per class. The validation set contains 372 examples (124 per class). The test set contains 9 unlabeled images per class. (Note: in the source, Laurence calls "validation" as the "test," and "test" the "validation.")

Examples

Rock

Paper

Scissors

Source

Flowers_Classification

Overview

The Flowers dataset is a classification detection dataset various flower species like dandelions and daisies.

Example Image:
Example Image

Use Cases

Build a flower classifier model! Consider deploying that to a mobile app for outdoor enthusiasts or florist hobbyists.

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.

Roboflow Workmark

Hyper-Kvasir

  • Simula
  • GI-tract Dataset
  • 10662 images

Overview
This is the largest Gastrointestinal dataset generously provided by Simula Research Laboratory in Norway

You can read their research paper here in Nature

In total, the dataset contains 10,662 labeled images stored using the JPEG format. The images can be found in the images folder. The classes, which each of the images belong to, correspond to the folder they are stored in (e.g., the ’polyp’ folder contains all polyp images, the ’barretts’ folder contains all images of Barrett’s esophagus, etc.). Each class-folder is located in a subfolder describing the type of finding, which again is located in a folder describing wheter it is a lower GI or upper GI finding. The number of images per class are not balanced, which is a general challenge in the medical field due to the fact that some findings occur more often than others. This adds an additional challenge for researchers, since methods applied to the data should also be able to learn from a small amount of training data. The labeled images represent 23 different classes of findings.

The data is collected during real gastro- and colonoscopy examinations at a Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists.

Use Cases

"Artificial intelligence is currently a hot topic in medicine. The fact that medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel to perform the cumbersome and tedious labeling of the data, leads to technical limitations. In this respect, we share the Hyper-Kvasir dataset, which is the largest image and video dataset from the gastrointestinal tract available today."

"We have used the labeled data to research the classification and segmentation of GI findings using both computer vision and ML approaches to potentially be used in live and post-analysis of patient examinations. Areas of potential utilization are analysis, classification, segmentation, and retrieval of images and videos with particular findings or particular properties from the computer science area. The labeled data can also be used for teaching and training in medical education. Having expert gastroenterologists providing the ground truths over various findings, HyperKvasir provides a unique and diverse learning set for future clinicians. Moreover, the unlabeled data is well suited for semi-supervised and unsupervised methods, and, if even more ground truth data is needed, the users of the data can use their own local medical experts to provide the needed labels. Finally, the videos can in addition be used to simulate live endoscopies feeding the video into the system like it is captured directly from the endoscopes enable developers to do image classification."

Borgli, H., Thambawita, V., Smedsrud, P.H. et al. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data 7, 283 (2020). https://doi.org/10.1038/s41597-020-00622-y

Using this Dataset

Hyper-Kvasir is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source. This means that in all documents and papers that use or refer to the Hyper-Kvasir dataset or report experimental results based on the dataset, a reference to the related article needs to be added: PREPRINT: https://osf.io/mkzcq/. Additionally, one should provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

About Roboflow

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

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

Fruits Dataset

Overview

The Fruits dataset is an image classification dataset of various fruits against white backgrounds from various angles, originally open sourced by GitHub user horea. This is a subset of that full dataset.

Example Image:
Example Image

Use Cases

Build a fruit classifier! This could be a just-for-fun project just as much as you could be building a color sorter for agricultural use cases before fruits make their way to market.

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.

Roboflow Workmark

Chest X-Rays

This classification dataset is from Kaggle and was uploaded to Kaggle by Paul Mooney.

It contains over 5,000 images of chest x-rays in two categories: "PNEUMONIA" and "NORMAL."

  • Version 1 contains the raw images, and only has the pre-processing feature of "Auto-Orient" applied to strip out EXIF data, and ensure all images are "right side up."
  • Version 2 contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied
  • Version 3 was trained with Roboflow's model architecture for classification datasets and contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied + augmentations:
    • Outputs per training example: 3
    • Shear: ±3° Horizontal, ±2° Vertical
    • Saturation: Between -5% and +5%
    • Brightness: Between -5% and +5%
    • Exposure: Between -5% and +5%

Below you will find the description provided on Kaggle:

Context

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
Figure S6
Figure S6. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6
The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs.
http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

Content

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

Acknowledgements

Data: https://data.mendeley.com/datasets/rscbjbr9sj/2

License: CC BY 4.0

Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
citation - latest version (Kaggle)

Inspiration

Automated methods to detect and classify human diseases from medical images.