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# Actiquest Human Vision Subnet on Bittensor

The rapid advancements in AI and computer vision have led to an increasing demand for scalable, decentralized platforms capable of supporting training and deployment of various vision models that can live efficiently on-device/edge hardware. The proposed Actiquest Human
Vision Subnet on Bittensor is designed to address these challenges by leveraging the decentralized nature of Bittensor's blockchain-based ecosystem. This subnet enables collaborative training of human vision models, promoting resource-sharing, innovation, and accessibility for researchers and developers worldwide.

# Actiquest Vision Subnet Overview

The Human Vision Subnet is a specialized layer within the Bittensor network that focuses exclusively on the training and optimization of vision models. By utilizing decentralized computing resources, this subnet creates a global marketplace for AI training tasks, ensuring efficiency, scalability, and robustness.

# Key Features

  1. Decentralized Training Environment:
  • Nodes contribute computational resources for training vision models collaboratively.
  • Rewards are distributed based on contributions, incentivizing participation.
  1. Model Interoperability:
  • Supports popular vision model architectures such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models.
  • Compatibility with major frameworks like TensorFlow, PyTorch, and ONNX.
  1. Human Vision Models:
  • Focuses on the development and optimization of vision models tailored for human biomechanics and behavior.
  • These models are directly designed to support applications in Actiq's AI Live Pod and other edge devices, enabling features such as sports analytics, physical therapy, movement science, and real-time feedback on user performance.
  1. Scalable Dataset Management:
  • Integrates with decentralized storage solutions for managing large datasets securely and efficiently.
  • Enables contributors to share and access high-quality, labeled datasets.
  1. Privacy and Security:
  • Ensures data privacy through advanced encryption and federated learning methodologies.
  • Protects intellectual property and sensitive data during model training and inference.
  1. Tokenized Incentive Mechanism:
  • Utilizes Bittensor's token ($TAO) for rewarding contributions to model training, dataset provision, and subnet operation.
  1. Incentivization Algorithm:
  • Nodes contributing computational resources or datasets are rewarded using a hybrid algorithm:
    - Dynamic Load Balancing: Nodes are ranked by their computational contributions and assigned tasks based on their capacity and availability. Nodes with higher uptime and performance metrics are prioritized.
    - Performance-Based Rewards: Contribution metrics such as processing speed, accuracy of model updates, and dataset quality determine the reward distribution.
    - Task Weighting: More complex training tasks or those requiring large datasets provide higher rewards to incentivize participation in challenging workloads.
    - Feedback Loop: Nodes receive performance feedback, enabling them to optimize their configurations for future tasks and earn higher rewards.

# Benefits of the Human Vision Subnet

# For Developer

  1. Lower Barriers to Entry:
  • Access to decentralized resources reduces the cost of training vision models.
  • Shared datasets and pre-trained models accelerate development.
  1. Collaborative Innovation:
  • A global community fosters the co-creation of novel architectures and algorithms.

# For Node Operators

  1. Reward Opportunities:
  • Nodes earn $TAO tokens by contributing compute power and participating in subnet operations.
  1. Diverse Workloads:
  • Vision models introduce new workloads, diversifying earning opportunities for operators.

# For Businesses

  1. Cost-Effective Solutions:
  • Decentralized training eliminates the need for expensive, centralized infrastructure.
  • Enhanced scalability supports business-specific vision AI applications.
  1. Customizable Models:
  • Businesses can leverage community-trained models and fine-tune them for proprietary use cases.

# Use Cases

# 1. Biomechanics Analysis in Healthcare, Fitness and Sports

  • Problem: High-performance sports and movement analysis demand accurate and adaptable human biomechanics models.
  • Solution: The Vision Subnet facilitates the creation of biomechanics-focused vision models, enabling precision analytics for athletic training, injury prevention, and rehabilitation. These models are a core component of Actiq's AI Live Pod, providing real-time coaching and personalized feedback to users during fitness or wellness tasks.

# 2. Augmented and Virtual Reality

  • Problem: Enhancing AR/VR applications requires computationally intensive vision models.
  • Solution: The subnet's distributed resources lower the costs and time required to train these models.

# 3. Human Work Video Feed Annotation/Labeling

  • Problem: Minimise mistakes at the assembly line.
  • Solution: Vision subnet will train custom models, identify what the worker could improve and where they create or experience bottlenecks and determines whether a worker took the correct steps and did so in the correct order.

# Technical Implementation

# Architecture

  1. Node Structure:
  • Nodes are categorized into trainer nodes (providing computational resources) and dataset nodes (storing and sharing datasets).
  1. Consensus Mechanism:
  • Bittensor’s proof-of-contribution ensures fair rewards for nodes based on their participation.

# Model Training Workflow

  1. Dataset Initialization: Contributors upload datasets to decentralized storage.
  2. Model Sharing: Developers propose model architectures for training.
  3. Collaborative Training: Nodes collaboratively train the models, sharing gradients and updates.
  4. Validation and Deployment: Trained models are validated, token rewards are distributed, and the models are made available for use.

# Roadmap

# Phase 1: Network Initialization

  • Launch the Vision Subnet and onboard initial nodes.
  • Integrate with decentralized storage provider.

# Phase 2: Developer Onboarding

  • Support major vision model frameworks.
  • Provide tools and APIs for seamless integration.

# Phase 3: Expansion and Optimization

  • Enhance scalability and security features.
  • Introduce advanced tokenomics to incentivize long-term participation.

# Conclusion

The Human Vision Subnet on Bittensor represents a groundbreaking approach to training and deploying vision models in a decentralized, collaborative ecosystem. By addressing key challenges in AI development and fostering global participation, this subnet paves the way for accessible, scalable, and innovative computer vision solutions. With its emphasis on human biomechanics and integration into Actiq's AI Live Pod, the Vision Subnet is poised to unlock new possibilities in sports, healthcare, and beyond. Join the revolution and shape the future of decentralized AI!