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hadryan's Projects

tencent-ml-images icon tencent-ml-images

Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet

tendis icon tendis

Tendis is a high-performance distributed storage system fully compatible with the Redis protocol.

tengine icon tengine

Tengine is a lite, high performance, modular inference engine for embedded device

tensorflowtts icon tensorflowtts

:stuck_out_tongue_closed_eyes: TensorflowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2

terraform icon terraform

Terraform enables you to safely and predictably create, change, and improve infrastructure. It is an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.

tesseract.js icon tesseract.js

Pure Javascript OCR for more than 100 Languages 📖🎉🖥

text2art icon text2art

AI-powered Text-to-Art Generator - Text2Art.com

texthero icon texthero

Text preprocessing, representation and visualization from zero to hero.

textrecognizer icon textrecognizer

This project is aimed at developing software that provides the features : image file to text file convertor, text file to image file convertor, text file to audio file convertor and image file (containing text) to audio file.

textshot icon textshot

Python tool for grabbing text via screenshot

textual icon textual

Textual is a TUI (Text User Interface) framework for Python inspired by modern web development.

tfnet-for-environmental-sound-classification icon tfnet-for-environmental-sound-classification

Learning discriminative and robust time-frequency representations for environmental sound classification: Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.

thanos icon thanos

Highly available Prometheus setup with long term storage capabilities. A CNCF Incubating project.

the-backbone-store icon the-backbone-store

A simple application, a JSON store of products and lists of products, as a demonstration using Backbone.js.

the-book-of-secret-knowledge icon the-book-of-secret-knowledge

:dizzy: A collection of awesome lists, manuals, blogs, hacks, one-liners, cli/web tools and more. Especially for System and Network Administrators, DevOps, Pentesters or Security Researchers.

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