Welcome to the BitMind Subnet! This repository contains all the necessary information to get started, understand our subnet architecture, and contribute.
The BitMind platform offers a best-in-class developer experience for Bittensor miners.
⚡ Access Compute: We offer a wide variety of CPU and GPU options
⚡ Develop in VSCode: Develop in a feature-rich IDE (we support Jupyter too if you hate rich features)
⚡ Fully Managed Devops: No more tinkering with networking configuration - register and deploy your miner in just a few clicks
⚡ Monitor Emissions: View the emissions for all of your miners in our Miner Dashboard
- Mining Guide ⛏️
- Validator Guide 🔧
- Incentive Mechanism 📈
- Project Structure and Terminology 📖
- Contributor Guide 🤝
IMPORTANT: If you are new to Bittensor, we recommend familiarizing yourself with the basics on the Bittensor Website before proceeding to the Setup Guide page.
Overview: The BitMind Subnet leverages advanced generative and discriminative AI models within the Bittensor network to detect AI-generated images. This platform is engineered on a decentralized, incentive-driven framework to enhance trustworthiness and stimulate continuous technological advancement.
Purpose: The proliferation of generative AI models has significantly increased the production of high-quality synthetic media, presenting challenges in distinguishing these from authentic content. The BitMind Subnet addresses this challenge by providing robust detection mechanisms to maintain the integrity of digital media.
Features:
- Applications: See our applications page for a list of applications that leverage the BitMind Subnet to detect AI-generated media
- Model Evolution: Our platform continuously integrates the latest research and developments in AI to adapt to evolving generative techniques.
Core Components:
- Miners: Tasked with running binary classifiers that discern between genuine and AI-generated content.
- Research Integration: We systematically update our detection models and methodologies in response to emerging academic research, offering resources like training codes and model weights to our community.
- Validators: Responsible for challenging miners with a balanced mix of real and synthetic images, drawn from a diverse pool of sources.
- Resource Expansion: We are committed to enhancing the validators' capabilities by increasing the diversity and volume of the image pool, which supports rigorous testing and validation processes.
Subnet Architecture Diagram
For real-time discussions, community support, and regular updates, join our Discord server. Connect with developers, researchers, and users to get the most out of BitMind Subnet.
This repository is licensed under the MIT License.
# The MIT License (MIT)
# Copyright © 2023 Yuma Rao
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the “Software”), to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
# the Software.
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
# THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.