firstelfin's repositories
AddressBook
Base of C++ 01
BboxTransform
Some measures to deal with Bbox
NLP_Question_Answering
Sports question answering system based on partial network news materials
cv-papers
计算机视觉相关论文整理、记录、分享; 包括图像分类、目标检测、视觉跟踪/目标跟踪、人脸识别/人脸验证、OCR/场景文本检测及识别等领域。欢迎加星,欢迎指正错误,同时也期待能够共同参与!!! 持续更新中... ...
darknet
YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )
DeepNude_NoWatermark_withModel
DeepNude source code,without watermark,with demo and model download link,one command to run offline,GAN/Pytorch/pix2pix/pic2pic
detectron2
Detectron2 for Document Layout Analysis
ELSDc
Ellipse and Line Segment Detector, with Continuous validation
firstelfin.github.io
用于博客发布
Loss
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.
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
noise2noise
Noise2Noise: Learning Image Restoration without Clean Data - Official TensorFlow implementation of the ICML 2018 paper
PythonLogging
Python Project Log usage test
SegLoss
A collection of loss functions for medical image segmentation
service-streamer
Boosting your Web Services of Deep Learning Applications.
tpu
Reference models and tools for Cloud TPUs.
transformers
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.