sagarvegad / Video Classification Cnn And Lstm
To classify video into various classes using keras library with tensorflow as back-end.
Stars: ✭ 218
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Video Classification Cnn And Lstm
Predictive Maintenance Using Lstm
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
Stars: ✭ 352 (+61.47%)
Mutual labels: deep-neural-networks, lstm, keras-models
Bitcoin Price Prediction Using Lstm
Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network
Stars: ✭ 67 (-69.27%)
Mutual labels: deep-neural-networks, lstm
Irm Based Speech Enhancement Using Lstm
Ideal Ratio Mask (IRM) Estimation based Speech Enhancement using LSTM
Stars: ✭ 66 (-69.72%)
Mutual labels: deep-neural-networks, lstm
Time Attention
Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971
Stars: ✭ 52 (-76.15%)
Mutual labels: deep-neural-networks, lstm
Bidaf Keras
Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2
Stars: ✭ 60 (-72.48%)
Mutual labels: deep-neural-networks, keras-models
Jlm
A fast LSTM Language Model for large vocabulary language like Japanese and Chinese
Stars: ✭ 105 (-51.83%)
Mutual labels: deep-neural-networks, lstm
Ner Lstm
Named Entity Recognition using multilayered bidirectional LSTM
Stars: ✭ 532 (+144.04%)
Mutual labels: deep-neural-networks, lstm
Pytorch convlstm
convolutional lstm implementation in pytorch
Stars: ✭ 126 (-42.2%)
Mutual labels: deep-neural-networks, lstm
Pytorch Kaldi
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
Stars: ✭ 2,097 (+861.93%)
Mutual labels: deep-neural-networks, lstm
Speech Emotion Recognition
Speaker independent emotion recognition
Stars: ✭ 169 (-22.48%)
Mutual labels: deep-neural-networks, lstm
Deepseqslam
The Official Deep Learning Framework for Route-based Place Recognition
Stars: ✭ 49 (-77.52%)
Mutual labels: deep-neural-networks, lstm
Sangita
A Natural Language Toolkit for Indian Languages
Stars: ✭ 43 (-80.28%)
Mutual labels: deep-neural-networks, lstm
Gdax Orderbook Ml
Application of machine learning to the Coinbase (GDAX) orderbook
Stars: ✭ 60 (-72.48%)
Mutual labels: deep-neural-networks, lstm
Deep Learning Time Series
List of papers, code and experiments using deep learning for time series forecasting
Stars: ✭ 796 (+265.14%)
Mutual labels: deep-neural-networks, lstm
Pytorch Learners Tutorial
PyTorch tutorial for learners
Stars: ✭ 97 (-55.5%)
Mutual labels: deep-neural-networks, lstm
Datastories Semeval2017 Task4
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis".
Stars: ✭ 184 (-15.6%)
Mutual labels: lstm, keras-models
Easy Deep Learning With Keras
Keras tutorial for beginners (using TF backend)
Stars: ✭ 367 (+68.35%)
Mutual labels: deep-neural-networks, lstm
Flow Forecast
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Stars: ✭ 368 (+68.81%)
Mutual labels: deep-neural-networks, lstm
Keras transfer cifar10
Object classification with CIFAR-10 using transfer learning
Stars: ✭ 120 (-44.95%)
Mutual labels: keras-models, cnn-model
Video-Classification-CNN-and-LSTM
To classify videos into various classes using keras library with tensorflow as back-end.
I have taken 5 classes from sports 1M dataset like unicycling, marshal arts, dog agility, jetsprint and clay pigeon shooting. First I have captured the frames per sec from the video and stored the images. I gave the labels to those images and trained them on VGG16 pretrained model.
I achieved 78% accuracy on frames using CNN model, 73% accuracy on whole videos using CNN model, 81% accuracy on frames using CNN-LSTM architecture, 77% accuracy on videos using CNN-LSTM.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].