model-optimization

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.

Efficient in-memory representation for ONNX, in Python

Code of the ICASSP 2022 paper "Gradient Variance Loss for Structure Enhanced Super-Resolution"

Ralph Loop Optimizer: an AI-driven framework that turns any evaluatable codebase into a self-improving optimization loop for strategies, models, prompts, and workflows

本仓库包含了完整的深度学习应用开发流程，以经典的手写字符识别为例，基于LeNet网络构建。推理部分使用torch、onnxruntime以及openvino框架💖

Mobile AI: iOS CoreML, Android TFLite, on-device inference, ONNX, TensorRT, and ML deployment for smartphones.

Adaptive MoE inference for Kimi K3 — beyond-memory expert streaming, reversible runtime profiles, measured optimization results, and an open NVIDIA/GPU/NPU adaptation roadmap.

ptdeco is a library for model optimization by matrix decomposition built on top of PyTorch

Vision-lanugage model example code.

Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks

First thermal super-resolution system to achieve 34.2 dB PSNR at 229+ FPS using novel IMDN architecture with specialized thermal adaptations. Features breakthrough RGB→thermal transfer learning, thermal-aware multi-component loss, and real-time inference (2x: 270.6 FPS, 3x: 256.1 FPS, 4x: 250.9 FPS). Production-ready PyTorch + CUDA implementation

DA2Lite is an automated model compression toolkit for PyTorch.

Convert and optimize BirdNET models for ONNX Runtime inference on GPUs, CPUs, and embedded devices

40x faster AI inference: ONNX to TensorRT optimization with FP16/INT8 quantization, multi-GPU support, and deployment

A hardware-agnostic profiler for tracking the FLOPs and BOPs of Machine and Deep Learning algorithms.

Pytorch-TurboQuant: High-performance weight-only quantization for PyTorch. Optimized for fast inference and reduced memory footprint.

compares different pretrained object classification with per-layer and per-channel quantization using pytorch

Deploy PyTorch vision models as fast, memory-safe Rust binaries

Automated NPU fallback diagnosis - find which layers fell back to CPU, on which target, and why.