ai1361720220000,Alxendx,github

deep_sort_pytorch

MOT tracking using deepsort and yolov3 with pytorch

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Direct Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.

dunet-retinal-vessel-detection

A deformable-Unet architecture for retinal vessel segmentation

erf-pspnet

ERF-PSPNET implemented by tensorflow

fabric-defects-classification

The project completed the texture feature extraction and fabric defect image classification based on WLD and optimized WLD. Edit Add topics

faster-rcnn-tensorflow-python3.5

Tensorflow Faster R-CNN for Windows and Python 3.5

faster-rcnn_tf

Faster-RCNN in Tensorflow

fse

Feature Selection and Extraction

fundus-vessel-segmentation-tbme

In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.

garbage_classify

最严垃圾分类政策自7月1日颁布，如何进行垃圾分类已经成为居民生活的灵魂拷问。但是，没关系！AI在垃圾分类的应用可以成为居民的得力助手。本次垃圾分类挑战杯，目的在于构建基于深度学习技术的图像分类模型，实现垃圾图片类别的精准识别，大赛参考深圳垃圾分类标准，按可回收物、厨余垃圾、有害垃圾和其他垃圾四项分类。

gfte

A GCN-based table structure recognition method

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.