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2018-phm-data-challenge

2018 phm data challenge, ion mill machine RUL & fault diagnosis

aidc-2018-timeseries

Deep learning for time-series data

automated_leaf_segmentation

In order to create a suitable database of plant images, compromising of healthy and diseased images, we need to obtain images of plant leaves in a white background. We are using image processing segmentation algorithms to achieve this task.

boston-housing-forecast

Using Numpy to implement a Boston house price prediction program

cnn-rnn-lstm-image-recognition

A deep learning project written in PyTorch, intended as a comparison between a convolutional neural network, recurrent neural network and ConvNet + LSTM for image recognition on MNIST dataset.

convrnn_for_rul_estimation

Code used in Thesis "Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems".

covid-ct

cvppp17_solution

This is a solution intended for my interview at bowery farms

cvpppnet

cvppp test

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deeplearningtutorial

深度学习代码

deepplantphenomics

Deep learning for plant phenotyping.

forecasting-model-of-housing-price-in-shanghai

An integral project in Data Mining, mainly using Machine Learning.

housing-forecast

hub

A library for transfer learning by reusing parts of TensorFlow models.

image-classification-transfer-learning-with-inception-v3

Transfer learning example based on Inception-v3 image recognition neural network.

instance-segmentation-cv

Pipeline for leaf counting through instance segmentation.

keras-transfer-learning-for-oxford102

Keras pretrained models (VGG16, InceptionV3, Resnet50, Resnet152) + Transfer Learning for predicting classes in the Oxford 102 flower dataset

l2t-ww

Learning What and Where to Transfer (ICML 2019)

labelimg

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

leaf-counting

Code for the paper Leveraging multiple datasets for deep leaf counting.

leaf-counting-1

Detection and Regression with Convolutional Neural Networks

leaf-segmentation-challenge-lsc

My friends Nikhil Vijay S, Harish Kumar Patidar and I did work on the leaf segmentation challenge, Mentored by Mr.Mohit Agarwal on an internship at Bennett University. In this project, we investigate the problem of segmenting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of- the-art deep learning architectures: UNET, a convolutional neural network for initial segmentation. Evaluation is performed on the leaf segmentation challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets.

leaf_count

Counting leaves in plant

leaf_square

This script counts approximate square of plant leafs on a photo

maizeleafcounting

Python scripts used in the maize leaf counting paper

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mask_rcnn-master--trainowerdatset

使用keras版本的Mask-RCNN来训练自己的数据，通过代码把样本制作麻烦的步骤变成简单方便。

mobilenet

MobileNet with Re-training/Fine-tuning and Center/Triplet Loss

models

Models and examples built with TensorFlow