Overview

The Playing Cards dataset is a collection of synthetically generated cards blended into various types of backgrounds. You will be able to perform object detection to detect both number and suit of the cards.

Example Footage

Training and Deployment

The playing cards model has been trained in Roboflow, available for inference on the Dataset tab.

One could also build a Card Counting model for either Black Jack or Poker using YOLOR. This is achieved using the Roboflow Platform which you can deploy the model for robust and real-time detections. You can learn more here: https://augmentedstartups.info/YOLOR-Get-Started

Video Demo using YOLOR for training- https://youtu.be/2lGTZuaH4ec

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. With over 90k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality.

Overview

The GDIT Aerial Airport dataset consists of aerial images containing instances of parked airplanes. All plane types have been grouped into a single classification named "airplane".

Example Image

About Streamlit

Streamlit is the fastest way to build and share data apps natively in Python.

About This Dataset

This dataset contains images of Streamlit logos that were contibuted by the Streamlit team and Creators 🎈.

Albeit small, this dataset does a great job of detecting Streamlit logos in real life.

To take training even further, this dataset was mixed with the Roboflow logos dataset, which includes null examples of other logos, preventing the model from over predicting 🚀

Projects Using This Dataset

This dataset is a partner project between Roboflow and Streamlit to show how to automatically detect Streamlit logos in a browser application. The application uses Streamlit's python based front end and makes post requests to this model's Roboflow Inference API.

The full open sourced code for this project will be released at the upcoming Streamlit + Roboflow webinar! Stay tuned!