Efficient in-memory representation for ONNX, in Python

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

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

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💖

Vision-lanugage model example code.

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

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

DA2Lite is an automated model compression toolkit for PyTorch.

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

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

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

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

Model quantization techniques for efficient LLM inference. Experiments with INT8, INT4, and mixed-precision quantization.

This project is built to detect spam messages using a Long Short-Term Memory (LSTM) model combined with Word2Vec as the word embedding technique. The model has been optimized using Grid Search, achieving a best accuracy of 95.65%.

Arbitrary Numbers

Optimized IDKL Model for Visible-Infrared Person Re-Identification focusing on efficiency for resource-constrained hardware.

Quantization for Object Detection in Tensorflow 2.x

Optimized ProteinMPNN for Apple Silicon: 15× speedup with 0% accuracy loss through architecture pruning, batching, and ANE acceleration. Comprehensive benchmarking study of speed-accuracy trade-offs.

Comprehensive performance analysis of DeepSeek V3 quantization levels (FP16, Q8_0, Q4_0) on 16GB GPU environments.