This network is a decentralized subnet designed to inference images to extract information such as construction sites. Built on Bittensor, this subnet incentivizes miners to develop and deploy models that accurately detect features, starting with construction sites.
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⛏️ Mining Guide 🔧 Validator Guide 🏗️ Architecture Diagrams 📈 Incentive Mechanism 🤝 Contributor Guide |
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This documentation assumes basic familiarity with Bittensor concepts. For an introduction, please check out the docs: https://docs.bittensor.com/learn/bittensor-building-blocks.
Miners
- Miners are tasked with running binary classifiers that discern between images with and without roadwork, and are rewarded based on their accuracy.
- Miners predict a float value in [0., 1.], with values greater than 0.5 indicating the image contains roadwork.
A unique feature of the NATIX Network is its dynamic reward system. Miners must submit their image classification models to a public repository on Hugging Face. The submission is valid for 90 days, during which their reward factor is one. As time progresses beyond this period, the reward factor decays to zero, incentivizing continuous improvement and re-submission of models.
Validators
- Validators challenge miners with a balanced mix of real and synthetic media drawn from a diverse pool of sources.
- We continually add new datasets and generative models to our validators in order to maximize coverage of the types of diverse data. Models and datasets are defined in
natix/validator/config.py.
Figure 1 (above): Ecosystem Overview
This diagram provides an overview of the validator neuron, miner neuron, and other components external to the subnet.
- The green arrows show how applications interact with the subnet to provide construction site detection functionality.
- The blue arrows show how validators generate challenges, present miners with images, and score their responses.
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