u/poidhxyz

Post written by a dev who recently deployed a fully onchain frontend for my project poidh: https://farcaster.xyz/acgk.eth/0x672d28dc

I'm working with a group who would be interested in potentially putting up a $25,000 prize for a specific computer vision breakthrough.

However, I am not anywhere close to an expert in computer vision, and they are not either, so we are looking for feedback on whether this prize makes sense.

We want to focus on incentivizing a small-but-powerful, open source vision model.

Current idea:

• The prize will go to the first team or individual to develop an open-source computer vision model under 10 MB that achieves at least 80% Top-1 accuracy on ImageNet-1K while running entirely offline on a Raspberry Pi 5.
• Maximum Model Size: ≤ 10,000,000 bytes (10 MB). This applies to the complete storage footprint required to execute inference, including model weights and the final model file format (.onnx, .tflite, .safetensors, etc.). External feature stores, hidden lookup tables, embedded auxiliary weights, or additional model files are prohibited.
• Performance Target: ≥80.0% Top-1 Accuracy on the official ImageNet-1K validation dataset using the standard evaluation protocol.
• Execution Architecture: Single-model submission only (no multi-model ensembles, cascades, or fallback models). Models must run using CPU-only inference and operate entirely offline without internet access.
• Target Hardware: Must successfully execute inference and complete evaluation on a Raspberry Pi 5 (8 GB RAM) running a standard 64-bit OS.
• Open Source Requirements: Public GitHub repository containing complete model weights, training pipeline code, inference code, and an independent reproducible evaluation script.
• Licensing: Fully released under a permissive MIT or Apache 2.0 license.
• Integrity: Models must rely on generalized computer vision features. Any submission discovered to be hardcoded, overfitted to, or otherwise gaming the ImageNet-1K validation set will be immediately disqualified.

Are these requirements reasonable? Too easy? Too hard to judge? And if they don't make sense, can anyone point me to a clear, specific barrier in computer vision that fits the focus on supporting efficient open source models?