Minimalistic, hackable PyTorch implementation of SimSiam in ~400 lines. Achieves good performance on ImageNet with ResNet50. Features distributed training, real-time KNN eval, and AMP. Perfect for research prototyping.
This project is a production-grade autonomous control system designed to maintain machine learning model integrity through a closed-loop Detect → Diagnose → Decide → Act → Explain cycle. Unlike traditional monitoring that requires slow human intervention, SHMLP autonomously identifies data drift, concept shift, and inference anomalies to execute
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