Skip to main content

synthetic datasets for benchmarking AI and machine learning

Project description



# Synthetic Datasets

## Installation

```
pip install synthetic-datasets
```

## Datasets

* NoiseCircle


### NoiseCircle

A generator of square images, by default 64x64, with static noise and a circle
with noisy pixels in the image at a random location and with a random size.

Each result from the generator is a square numpy matrix of type float32

Example use::

```python
from synthetic_datasets import NoiseCircle

nc = NoiseCircle(batch_size=32, dim=64)
for samples, labels in nc:

// samples is a (32, 64, 64) numpy array of noise circle images
// labels is a dict with three keys, "X", "Y", and "R".
// These represent the X, Y, and RADIUS (in pixels) of the circle in the image.
// Each key holds a numpy array of shape (32,)
```



## Licence
MIT


## More info
- https://github.com/synthetic-datasets/synthetic-datasets
- https://www.meetup.com/Toronto-AI/
- http://torontoai.org/
- A Toronto AI initiative


Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
synthetic_datasets-0.1.11-py3-none-any.whl (3.3 kB) Copy SHA256 hash SHA256 Wheel py3
synthetic_datasets-0.1.11.tar.gz (2.5 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page