Setup

git clone git@github.com:BitMind-AI/bitmind-subnet-utils.git
cd bitmind-subnet-utils && pip install -r requirements.txt

Features

• W&B Downloads: Fetch validator runs from w&b for analysis
• Performance Analysis: Compute comprehensive metrics including:
  • Multiclass accuracy, precision, recall, F1, MCC
  • Binary accuracy, precision, recall, F1, AUC, MCC
• Media Management: Download and organize challenge media (images/videos)
• Visual Gallery: Generate HTML galleries showing:
  • Challenge media
  • Predictions from multiple miners
  • Performance metrics and metadata
  • Color-coded correctness indicators

File Structure

• wandb_explore.ipynb: Main notebook with examples and usage
• data.py: Core functions for data fetching and processing
• metrics.py: Performance metric calculations
• media_gallery.py: HTML gallery generation
• requirements.txt: Project dependencies

Output Files

The tools generate several output files:

• miner_predictions.csv: Detailed prediction data
• miner_performance.csv: Aggregated performance metrics
• Downloaded media files in the specified download directory
• media_gallery.html: Visual gallery of challenge media and predictions

Example media gallery

License

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
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# 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
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