Tensorflow impementation of Fully Convolutional Network (FCN) for Image Segmentation on both Kitti/Cityscapes dataset

model, training code, and thoughts from training an UNet

Udacity Self-Driving Car Engineer Nanodegree Semantic Segmentation Project.

Implementing FCN8 and FCN32 semantic segmentation models to classify pixels in road scenes.

The transition from primary to secondary protein structure involves the folding of linear amino acid sequences (primary structure) into regular patterns like alpha-helices and beta-sheets (secondary structure). Deep learning-based prediction algorithms leverage neural network architectures to infer these patterns from primary sequence data.

[Caffe] A deep convnet developed for semantic segmentation task.

A project on Topic Modelling and Text Summarization of NIPS research papers

TensorFlow-based semantic segmentation codes.

An approach for pixel-based classification of drivable parts of the road (known as Semantic Segmentation). Project 12 of Udacity's Self-Driving Car Engineer Nanodegree Program.

FCN model and utils for automating the segmentation of knee structures from MR images

Semantic Segmentation on the Indian Driving Dataset for the NVCPRIPG 2019 Challenge

Data Mining Course Assignments - Fall 2019

RoboND Term 1 Deep Learning Project, Follow-Me

Use Fully Convolutional Nets to identify segments of image as drivable road

Fully Connected Convolutional Neural Net FCN to identify the road in a collection of pictures. The encoding of the FCN will be provided by a pre-trained VGG16 model and the decoder will be built using 1x1 convolutions, upscaling and layer skipping.

Fully Convolutional Image Segmentation based on ResNet.

Fully Convolutional Networks for Liver Segmentation in TensorFlow

3D obstacle avoidance using perception