Quantized Cellpose-SAM: 6.76x Smaller With No Failures on Stem Cell Microscopy
gsarti_ · x · 2026-09-21
capicu-ai presents a deployment-oriented post-training quantization evaluation of Cellpose-SAM, aiming to run the iPSC microscopy segmentation foundation model on lab CPUs and edge hardware, with an auditable compression acceptance criterion.
Setup: a pre-specified retention criterion — the 95% cluster-bootstrap interval of mean change from FP32 must stay above a fixed -0.02 margin for every imaging modality. On a stratified 176-field panel spanning BBBC038 nuclei, BBBC039 U2OS fluorescence and NIST iPSC images across density regimes:
- Weight-only W8A16 preserves instance F1 across all modalities;
- A sensitivity-guided mixed W4/W8 scheme (only four INT8 exceptions) achieves a 6.76x reduction in weight storage with 0/176 catastrophic failures, matching W8A16 at this sample size;
- Ternary weight-only quantization reaches 12.08x compression but fails catastrophically on 169/176 fields.
Takeaway: compressed foundation models should be evaluated by modality-stratified downstream retention rather than single-number accuracy; the paper also establishes a reproducible protocol for auditing compressed models in regulated stem-cell imaging.
Related event: Cellpose-SAM Quantization Achieves 6.76x Compression With Zero Failures(2 posts)→
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