DeepLabV3-ResNet50 Tunnel Defect Segmentation (optional)

A library to help with the development of AI models with Keras, with a focus on edge / IoT applications. Based originally on https://github.com/yingkaisha/keras-unet-collection

Coding Neural Network architectures using PyTorch

DeepLabV3-3D model used on Sentinel2-Munich480 Dataset.

Cloud Cover Detection using Deep Learning

An in-depth analysis of deep learning models U-Net and DeepLabV3+ in semantic segmentation, highlighting their applications in urban plaing, environmental monitoring, and geographic information systems.

Performance of various image segmentation models.

scripts for training DeepLab using custom dataset

Semantic Segmentation Library (UNet, DeepLabV3)

Solution for ProKI-Hackathon 24. Fine-tuning of DeepLabV3 with ResNet101 backbone and Simulated Annealing.

Detection of the necessity of thinnings using aerial data sources and deep neural networks

PyTorch code for binary segmentation on CelebAMask-HQ dataset via both a UNet written from scratch and a pretrained DeepLabv3 model.

Urban segmentation on multi channel satellite images to identify Residential and Industrial built up areas

Road detection using DeepLabv3 segmentation model

The project utilizes convolutional-based network architectures for semantic image segmentation. This project utilizes the Indian Driving Dataset (IDD) Lite to classify various elements in Indian road scenes into 8 predefined categories. This implementation utilizes FCN - VGG backbone, U-Net, LINKNet, and DEEPLABV3+.

Deeplab v3 for Semantic Segmentation in NeuPy and Tensorflow

TinyML-aware spectrogram segmentation using pruning and knowledge distillation for RF spectrum sensing.