sivaramakrishnan-rajaraman
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Name: Sivaramakrishnan Rajaraman
Type: User
Company: National Library of Medicine, National Institutes of Health, USA
Bio: Dr. Sivarama Krishnan Rajaraman holds the position of Deep Learning Research Scientist at the Lister Hill Center, National Library of Medicine, NIH, USA.
Location: Bethesda, Maryland, USA
Blog: https://lhncbc.nlm.nih.gov/personnel/sivaramakrishnan-rajaraman
Sivaramakrishnan Rajaraman's Projects
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
:chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
This repository contains codes that I developed for image processing and evaluation of large dataset of images. These codes are mostly used with Deep Learning networks.
Custom image data generator for Keras supporting the use of modern augmentation modules
This repository contains images from 5 categories (250 for train and 50 for test for each category)
Matlab code for fast Hausdorff distance for binary images or segmentation maps
A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images, IEEE TMI 2020.
Keras implementation of layer which performs augmentations of images using GPU.
keras implementation of Faster R-CNN
Some State-of-the-Art few shot learning algorithms in tensorflow 2
Keras implementations of Generative Adversarial Networks.
Grey-scale Image Classification using KERAS
Image segmentation with keras. FCN, Unet, DeepLab V3 plus, Mask RCNN ... etc.
EDSR, RCAN, SRGAN, SRFEAT, ESRGAN
Examples of Keras loss functions
Some loss functions in Keras
[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.
Keras implementation of RetinaNet object detection.
Keras callback function for stochastic weight averaging
Pruning and other network surgery for trained Keras models.
Simple stochastic weight averaging callback for Keras
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
The Tensorflow, Keras implementation of Swin-Transformer and Swin-UNET
loss function for keras
Training and Detecting Objects with YOLO3
This is an attempt to implement neuro-fuzzy system on keras