DunguLock

DunguLock's Stars

• NVIDIA/TensorRT-LLM

  TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.

  Language:C++9k972.1k1k

• zhixuhao/unet

  unet for image segmentation

  Language:Jupyter Notebook4.6k1022352k

• foolwood/benchmark_results

  Visual Tracking Paper List

  3.9k35001k

• hwalsuklee/awesome-deep-text-detection-recognition

  A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.

  2.5k15113511

• jakeret/tf_unet

  Generic U-Net Tensorflow implementation for image segmentation

  Language:Python1.9k65270747

• sstephenson/eco

  Embedded CoffeeScript templates

  Language:CoffeeScript1.7k355470

• mlcommons/training

  Reference implementations of MLPerf™ training benchmarks

  Language:Python1.6k116323562

• zenorocha/voice-elements

  :speaker: Web Component wrapper to the Web Speech API, that allows you to do voice recognition and speech synthesis using Polymer

  Language:HTML1.4k5434256

• MarvinTeichmann/tensorflow-fcn

  An Implementation of Fully Convolutional Networks in Tensorflow.

  Language:Python1.1k5452432

• ClementPinard/FlowNetPytorch

  Pytorch implementation of FlowNet by Dosovitskiy et al.

  Language:Python86520108206

• xionghc/Facial-Expression-Recognition

  Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).

  Language:Python6452356192

• AstarLight/Satellite-Segmentation

  Language:Python6191680232

• martin-danelljan/ECO

  Matlab implementation of the ECO tracker.

  Language:MATLAB6133362247

• Tlntin/Qwen-TensorRT-LLM

  Language:Python590611552

• warmspringwinds/tf-image-segmentation

  Image Segmentation framework based on Tensorflow and TF-Slim library

  Language:Python5493543186

• bertinetto/cfnet

  [CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

  Language:Matlab5233038154

• yangxue0827/R2CNN_FPN_Tensorflow

  R2CNN: Rotational Region CNN Based on FPN (Tensorflow)

  Language:Python4192179139

• tkuanlun350/Tensorflow-SegNet

  Implement slightly different caffe-segnet in tensorflow

  Language:Python3693153164

• Shicoder/DeepLearning_Demo

  深度学习入门的一些简单例子

  Language:Python354266426

• tzirakis/Multimodal-Emotion-Recognition

  This repository contains the code for the paper `End-to-End Multimodal Emotion Recognition using Deep Neural Networks`.

  Language:Python2388876

• ChengBinJin/V-GAN-tensorflow

  A tensorflow implementation of "Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks"

  Language:Python233111849

• zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow

  An (re-)implementation of DeepLab v2 (ResNet-101) in TensorFlow for semantic image segmentation on the PASCAL VOC 2012 dataset.

  Language:Python17496488

• lifeng9472/STRCF

  Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (CVPR 2018)

  Language:MATLAB15381940

• allenai/dnw

  Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)

  Language:Python1388217

• mozilla/voicefill

  A WebExtension To Add Speech To Text Support to Web Pages

  Language:JavaScript921718729

• zeroQiaoba/EmotiW2018

  Language:Python895528

• spiglerg/DQN_DDQN_Dueling_and_DDPG_Tensorflow

  Tensorflow + OpenAI Gym implementation of Deep Q-Network (DQN), Double DQN (DDQN), Dueling Network and Deep Deterministic Policy Gradient (DDPG)

  Language:Python754432

• kousik97/Video-Expression-Recognition

  Recognising expression/emotion of unique faces in a video

  Language:Python19236

• deepak-ucfknight/Emotion-Recognition

  Emotion Classification on FerPlus Dataset

  Language:Python10002

• LeadingIndiaAI/Multimodal-Emotion-Recognition-in-Polish

  Multimodal emotion recognition is a challenging task because emotions can be expressed through various modalities. It can be applied in various fields, for example, human-computer interaction, crime, healthcare, multimedia retrieval, etc. In recent times, neural networks have achieved overwhelming success in determining emotional states. Motivated by these advancements, we present a multimodal emotion recognition system which is based on body language, facial expression and speech. This paper presents the techniques used in the Multimodal Emotion Recognition in Polish challenge. To detect the emotional state for various videos, data preprocessing operations are performed and robust features are extracted. For this purpose, we have used facial landmark detection for facial expressions and MFCC for speech.

  Language:Jupyter Notebook9002