firstelfin,github

We always encounter a lot of loss functions, but do we have to make our own wheels every time? Many scholars have encapsulated the common loss function, why not directly refer to the project? This repo is used to collect these excellent implementations. Contributions are welcome.

map

mean Average Precision - This code evaluates the performance of your neural net for object recognition.

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

micronet

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

moco-v3

PyTorch implementation of MoCo v3 https//arxiv.org/abs/2104.02057

nlp_question_answering

Sports question answering system based on partial network news materials

noise2noise

Noise2Noise: Learning Image Restoration without Clean Data - Official TensorFlow implementation of the ICML 2018 paper

portraits_correction

publaynet

python-common-functions

Some common functions of Python Programming

python-spider

pythonlogging

Python Project Log usage test

reducefpbyopticalflow

基于光流法的背景误报消除

segloss

A collection of loss functions for medical image segmentation

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.