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fast-shadow-detection

fastmri

A large-scale dataset of both raw MRI measurements and clinical MRI images

fastswa-semi-sup

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging

fcis

Fully Convolutional Instance-aware Semantic Segmentation

fcn-4s-for-building-extraction

Building Extraction based on FCN-4s

fcn-cd-pytorch

Training code for https://github.com/rcdaudt/fully_convolutional_change_detection

fcn.tensorflow

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

fcsn_for_changedetection_igarss2018

Fully Convolutional Siamese Networks for Change Detectionno

fdcnn

The implementation of FDCNN in paper - A Feature Difference Convolutional Neural Network-Based Change Detection Method

feat

The code repository for "Learning Embedding Adaptation for Few-Shot Learning"

feature-extractor

Feature Extraction program with Grammatical Evolution

features

IDL code for analyzing features in images

few-shot-gnn

fewrel

A Large-Scale Few-Shot Relation Extraction Dataset

fewshot

fewshot_blogpost

fewshot_gan-unet3d

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

fewshotlearning

fewshotwithoutforgetting

ffdnet

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)

figr

Few-shot Image Generation with Reptile

figr-8

Few-shot Image Generation with Reptile: the dataset

final-year-projrct

AUTOMATED TYPE CLASSIFICATION OF DIABETIC RETINOPATHY AND GLAUCOMA DETECTION USING DEEP LEARNING

fire-detection-uav-aerial-image-classification-segmentation-unmannedaerialvehicle

Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

foa-kmeans-color-image-segmentation

Clustering analysis using an evolutionary optimization algorithm based on nature, Forest Optimization Algorithm

fourier-mellin

Image Registration using Fourier-Mellin

fully-automated-method-for-glaucoma-screening-using-cup-to-disc-ratio-computation-in-retinal-images

Python implementation of the paper "Fully Automated Method for Glaucoma Screening Using Robust Optic Nerve Head Detection and Unsupervised Segmentation Based Cup-To-Disc Ratio Computation in Retinal Fundus Images", by Amed Mvoulana et al., published in Computerized Medical Imaging and Graphics, 2019.

fully_convolutional_change_detection

fully_unsupervised_cnn_registration_keras

Fully unsupervised image registration with CNN

fusiongan

FusionGAN: A generative adversarial network for infrared and visible image fusion