TinyMind
TinyMind is a deep learning framework for Unity that allows developers to build deep learning applications using C# without relying on third-party libraries.
TinyMind is a deep learning framework for Unity that allows developers to build deep learning applications using C# without relying on third-party libraries.* This page contains affiliate links, meaning we may earn a small commission if you purchase something through them, at no extra cost. $28
- Category:
- Tools › Ai-ml-integration
- Developer:
- tiny3d
- Price:
- $28
- Rating:
- Favorites:
- 13
- Supported Unity Versions:
- 2022.3.17 or higher
- Current Version:
- 0.2.0
- Download Size:
- 1.56 GB
- Last Update:
- Apr 8, 2025
- Description:
- Deep learning has become a popular direction in science, enabling applications such as image recognition, speech recognition, text translation, and object detection. However, most deep learning development is limited to Python. TinyMind changes this by allowing developers to build deep learning applications in Unity using the efficient and elegant C# language.
With TinyMind, you can perform various deep learning tasks, including image recognition, object detection, image generation, semantic segmentation, and reinforcement learning. As deep learning relies heavily on big data, third-party datasets are often used. TinyMind's demos utilize the following third-party datasets under the MIT license:
* FashionMNIST
* udacity_driving
* CeyMo
* P1_Facial_Keypoints
Please refer to the Third-Party Notices.txt file in the package for details.
TinyMind offers several key features:
* Dynamic Computation Graphs and Automatic Differentiation
* Modular design for easy neural network building
* High-performance computations using Burst and Job systems
* Common transforms to improve neural network training and inference accuracy
* Weight loading and saving capabilities - Technical Details:
- Key features of TinyMind include:
* Dynamic Computation Graphs and Automatic Differentiation
* Modular design for easy neural network building
* High-performance computations using Burst and Job systems
* Provides common transforms to improve the accuracy of neural network training and inference
* Weight loading and saving capabilities - Continue »