{"repo":"sylvesterkaczmarek/phisat2-trustworthy-onboard-ai","free":true,"listed":false,"github":"https://github.com/sylvesterkaczmarek/phisat2-trustworthy-onboard-ai","clone":"git clone https://github.com/sylvesterkaczmarek/phisat2-trustworthy-onboard-ai.git","description":"Trustworthy onboard satellite AI in PyTorch→ONNX→INT8 with calibration, telemetry, and a PhiSat-2 EO tile-filter demo.","language":"Python","stars":49,"topics":["cubesat","earth-observation","edge-ai","esa","onnx","quantization","satellites","onboard-ai","phisat-2","calibration"],"license":"MIT","category":"analytics","readme_excerpt":"PhiSat-2 Trustworthy Onboard AI Research demonstrator for deterministic Earth-observation tile triage, PyTorch to ONNX deployment, static INT8 quantization, conservative downlink fallback, telemetry, and model rollback. The workflow is inspired by onboard EO processing such as PhiSat-2, but it is independent software and is not ESA or PhiSat-2 flight code. At a glance The design separates model training, policy calibration, and final evaluation. A calibration policy is cryptographically bound to the model SHA-256 and cannot silently be reused with a different model. What is implemented - deterministic synthetic EO data generation with independent train , calib , and test splits - arbitrary multispectral band counts through NumPy tile stacks - deterministic TinyCNN training with architecture metadata stored in the checkpoint - PyTorch to FP32 ONNX export with ONNX validation and numerical equivalence check - static QDQ INT8 quantization with calibration data kept separate from final test data - held-out FP32 versus INT8 accuracy and prediction-agreement regression checks - calibrated event threshold targeting a requested event recall - optional deterministic temperature scaling on the calibration split - conservative fallback that downlinks low-confidence tiles and inference failures - model/policy hash binding - per-tile input and model SHA-256 telemetry - exact byte-level bandwidth accounting - atomic known-good model promotion and rollback - bounded watchdog without shell=T","default_branch":null,"files":null,"tree":[],"storefront":"/r/sylvesterkaczmarek","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sylvesterkaczmarek/phisat2-trustworthy-onboard-ai/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}