Hxwinchina

3d-DenseNet

3D Dense Connected Convolutional Network (3D-DenseNet for action recognition)

Action-Recognition

recognize actions from videos using machine learning classifier(s) and suitable features. You will use UCF sports action data set here http://crcv.ucf.edu/data/ucf_sports_actions.zip. UCF Sports dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery and GettyImages. The dataset includes a total of 150 sequences with the resolution of 720 x 480. The collection represents a natural pool of actions featured in a wide range of scenes and viewpoints. By releasing the data set we hope to encourage further research into this class of action recognition in unconstrained environments. Since its introduction, the dataset has been used for numerous applications such as: action recognition, action localization, and saliency detection. The dataset includes the following 10 actions. The figure above shows the a sample frame of all ten actions, along with their bounding box annotations of the humans shown in yellow.

action-recognition-using-3d-resnet

Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.

Ad-papers

Papers on Computational Advertising

apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

awesome-semantic-segmentation-pytorch

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

CaffeRemoteSensing

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

DenseASPP

DenseASPP for Semantic Segmentation in Street Scenes

DSB17_3d_lung_nodule_classifier

3d convnet for the classification of nodules/tumor in lung CT scans. Trained on Luna16 for Kaggle's 2017 data science bowl competition (result in top 3%)

DSB2017

The solution of team 'grt123' in DSB2017

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

FCNVMB-Deep-learning-based-seismic-velocity-model-building

Deep-learning inversion: A next-generation seismic velocity model building method

gluon-tutorials-zh

通过 MXNet / Gluon 来动手学习深度学习

Human-Action-Recognition-and-Video-Classification-using-SVM-and-Deep-CNN-

humanMotionClassification

Experiments in classifying human actions using the UCF action databased.

jlu-drcom-client

JLU Drcom Client

jsrt-parser

This is a data parser to obtain images and descriptions from JSRT database in a uniform format for deep learning applications.

kaggle-lung-cancer-classification

kaggle data science bowl 2017 solution

KITTI_SSD

SSD detection for KITTI

libary_seat_inquiry

lesson homework

Medical-Image-Classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

simple-faster-rcnn-pytorch

A simplified implemention of Faster R-CNN that replicate performance from origin paper

UCF101-Classification

Analyze and classify videos in the UCF101 dataset

YOLO_v3_tutorial_from_scratch

Accompanying code for Paperspace tutorial series "How to Implement YOLO v3 Object Detector from Scratch"