Detect trends in real time from social data, generate targeted email campaigns with AI, and measure their revenue impact in one platform.
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Updated
Mar 29, 2026 - Python
Detect trends in real time from social data, generate targeted email campaigns with AI, and measure their revenue impact in one platform.
Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.
A Keras port of Detector of Rotatable Bounding Boxes
using semantic segmentation to identify drivable path for self driving cars
Implementation of science project for my master's degree. Scripts for segmentation task: UNET, SEGNET, FCN-8, K-Mean
CNN experiments for Bachelor Thesis at BUT FIT
Implementation of semantic segmentation of FCN structure using KITTI road dataset😝😝😝
PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset
A integrated deep learning platform for hyperspectral classification by pytorch
PyTorch Implementation of Semantic Segmentation CNNs: This repository features key architectures like UNet, DeepLabv3+, SegNet, FCN, and PSPNet. It's crafted to provide a solid foundation for Semantic Segmentation tasks using PyTorch.
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help
红外弱小目标检测算法 Infrared Target Detection by Segmentation (Deeplearing Method)
Semantic segmentation models implemented in PyTorch
CNN architectures for multiclass semantic segmentation of esophageal diseases
A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.
Implementation of `Fully Convolutional Networks for Semantic Segmentation` by Jonathan Long, Evan Shelhamer, Trevor Darrell, UC Berkeley
Multi-Dimensional Analysis of Hate Speech Using BERT and Cluster Analysis
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