laplace674

laplace674's Stars

• tensorflow/tensorflow

  An Open Source Machine Learning Framework for Everyone

  Language:C++187k7.5k40.1k74.4k

• tensorflow/models

  Models and examples built with TensorFlow

  Language:Python77.3k2.7k7.3k45.7k

• xialeiliu/Awesome-Incremental-Learning

  Awesome Incremental Learning

  3.9k13349580

• hoya012/awesome-anomaly-detection

  A curated list of awesome anomaly detection resources

  2.8k1315517

• intel/caffe

  This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors.

  Language:C++84785265491

• cgvict/roLabelImg

  Label Rotated Rect On Images for training

  Language:Python846933226

• kvfrans/variational-autoencoder

  generate MNIST using a Variational Autoencoder

  Language:Python710238194

• lars76/kmeans-anchor-boxes

  k-means clustering with the Intersection over Union (IoU) metric as described in the YOLO9000 paper

  Language:Python5321121197

• stdcoutzyx/DeepID_FaceClassify

  Practise of DeepID for Face Classification

  Language:Python5314514223

• chrieke/InstanceSegmentation_Sentinel2

  🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis

  Language:Jupyter Notebook4001514124

• Byronnar/tensorflow-serving-yolov3

  本项目主要对原tensorflow-yolov3版本做了许多细节上的改进,增加了TensorFlow-Serving工程部署,训练了多个数据集，包括Visdrone2019, 安全帽等, 安全帽mAP在98%左右, 推理速度1080上608的尺寸大概25fps.

  Language:Python3859107129

• chen0040/keras-anomaly-detection

  Anomaly detection implemented in Keras

  Language:Python374255155

• calmiLovesAI/TensorFlow2.0_ResNet

  A ResNet(ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) implementation using TensorFlow-2.0.

  Language:Python325510101

• peteryuX/retinaface-tf2

  RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

  Language:Python275124597

• liux0614/yolo_nano

  Unofficial implementation of yolo nano

  Language:Python254123772

• srebuffi/residual_adapters

  Language:Python1706535

• stephen-v/tensorflow_alexnet_classify

  tensorflow_alexnet_classify (details http://www.cnblogs.com/vipyoumay/p/7686230.html)

  Language:Python1066751

• otenim/TensorFlow-OS-ELM

  A tensorflow implementation of OS-ELM (Online Sequential Extreme Learning Machine)

  Language:Python557117

• Abraham-M97/YOLOV3-NANO

  YOLOV3-NANO-Tensorflow

  Language:Python473516

• jr0th/segmentation

  Code for my master's thesis. Instance segmentation of fluorescence microscopy images with deep learning.

  Language:Jupyter Notebook424329

• nuclearboy95/Anomaly-Detection-Deep-SVDD-Tensorflow

  Tensorflow Implementation of Deep SVDD

  Language:Python352412

• imransalam/autoencoders_tf2.0

  Simple, denoising and variational autoencoders made in TensorFlow2.0

  Language:Python16301

• rkmalaiya/network_anomaly_detection_deep_learning

  This project has been conducted under the supervision of Dr. Jinoh Kim and Dr. Donghwoon Kwon at Texas A&M University-Commerce. The research outcome are published in the proceeding of IEEE ICNC 2018 (http://www.conf-icnc.org/2018/), with the title of “An Empirical Evaluation of Deep Learning for Network Anomaly Detection”.

  Language:Jupyter Notebook165208

• xiaorancs/xr-learn

  自己手动实现机器学习的经典算法,写出来才是真正的理解.

  Language:Python5104

• DeepFunProject/Deep-Text-Classification-Tensorflow2

  We have used CNN and LSTM to classify the sentences into good and bad categories.

  Language:Jupyter Notebook4101

• camilonar/incremental-eureka

  Project in tensorflow with a variation of RMSProp for incremental learning based in the selection of representative samples of a dataset

  Language:Python320

• Alexander-Guy/SlimeClimb

  Base for slime mold optimization algorithm

  Language:Jupyter Notebook2100

• parisDreyer/SlimeMold

  This is a test repo for AI experiments modeled after the SlimeMold organism

  Language:Python220

• MaxIsWell42/SlimeMoldPrediction

  Slime mold always tries to find the most efficient path to food while also exploring for more, and optimizes itself once found. This aims to apply graph theory to slime mold in order to possibly predict it's path.

  Language:Python1100

• qiqzhang/Mask-Yolo2

  build yolo2 from scratch tensorflow and instance segmentation like Mask-RCNN

  Language:Python1101