Popular GitHub Repositories for Fp16

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

NanoDet-Plus?Super fast and lightweight anchor-free object detection model. ?Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone?

oneAPI Deep Neural Network Library (oneDNN)

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

???YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

? Difficult algorithm, Simple code.

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model

Library for specialized dense and sparse matrix operations, and deep learning primitives.

Microsoft Automatic Mixed Precision Library

convert mmdetection model to tensorrt, support fp16, int8, batch input, dynamic shape etc.

food image to recipe with deep convolutional neural networks.

I transfer the backend of yolov3 into Mobilenetv1，VGG16，ResNet101 and ResNeXt101

InsightFace REST API for easy deployment of face recognition services with TensorRT in Docker.

Deep learning codes and projects using Python

An innovative library for efficient LLM inference via low-bit quantization

tensorflow implementation of Grad-CAM (CNN visualization)

Example code and documentation on how to get Stable Diffusion running with ONNX FP16 models on DirectML. Can run accelerated on all DirectML supported cards including AMD and Intel.

Flux diffusion model implementation using quantized fp8 matmul & remaining layers use faster half precision accumulate, which is ~2x faster on consumer devices.