25 skills found
zhaoweicai / MscnnCaffe implementation of our multi-scale object detection framework
ljzycmd / SimDeblurSimple framework for image and video deblurring, implemented by PyTorch
Ling-Bao / Mscnnmscnn crowd counting model implementation, source from "Multi-scale Convolution Neural Networks for Crowd Counting" write by Zeng L, Xu X, Cai B, et al.
amrzhd / EEG MSCNNThis project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals
xiaochus / MSCNNA Python 3 and Keras 2 implementation of MSCNN for people counting.
zzubqh / CrowdCount基于mscnn的人群密度估计
luanshiyinyang / MSCNNTensorflow2（Keras）复现论文"Multi-scale Convolution Neural Networks for Crowd Counting"实现人群密度估计
dishank-b / MSCNN Dehazing TensorflowThis is Tensorflow implementation of Single Image Dehazing via Multi-Scale Convolutional Neural Networks https://sites.google.com/site/renwenqi888/research/dehazing/mscnndehazing
julianyulu / Icassp2021 Mscnn SpuCode for our paper "Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention" (ICASSP 2021, co-first authorship)
computer-animation-perception-group / SEMG Based MscnnCode repo of the paper "A Multi-stream Convolutional Neural Network for sEMG-based Gesture Recognition in Musclecomputer interface".
raven-dehaze-work / MSCNN Keras"Single Image Dehazing via Multi-scale Convolutional Neural Networks" Implemented by Keras
leichenNUSJ / AAMandDCMThis project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2].  ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by chenleinj@njust.edu.cn ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .
zhouxxd999 / Transformer MSCNN For EEG使用Transformer和CNN网络对脑电运动进行分类
xiaofeng94 / MSCNNS For Monocular Depth EstimationThe Pytorch implementation for "A CNN-based Depth Estimation Approach with Multi-scale Sub-pixel Convolutions and A Smoothness Constraint" (ACCV 2018)
JoeyBGOfficial / MSCNN For Through The Wall Radar Human Activity RecognitionThis is the repository of through-the-wall radar human activity recognition using multi-sampling convolutional neural networks (MSCNN). Author: JoeyBG.
KrishnaDN / Emotion Recognition Using MSCNN SPUNo description available
xingzhiyuan000 / MSCNN BearingNo description available
yuho8818 / MSCnnClassifier Evualation项目对Keras中可复用的CNN模型进行封装，用户只需修改配置文件和模型数据，就可选用包括VGG-16,ResNet-50等CNN模型对图片数据进行分类，也可根据具体需求进行二次开发。
Soyen-Song / Multi Scale CNN Based MRF For Image SegmentationThe code is for image segmentation. The MSCNN.py is implemented using keras with tensorflow as backend. The *.m files are implemented on MATLAB.
Yuqing2018 / MSCNN CrowdCountingMSCNN人群密度统计
