A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

A pytorch based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

The repository contains the implementation of PCA + SVM and PCA + Hybrid(2D+3D) CNN implemenatation techniques on Hyperspectral Images(Indian Pines Dataset).

Classification of Hyperspectral images of Indian Pines dataset using a hybrid network of 3D and 2D CNN

This deep learning framework offers 1,000+ models (including 100+ specialized CNN-Transformer hybrids for 2D/3D multimodal images) with a modular design for fully customizable components: adaptive training optimization, data preprocessing, model restructuring, and visual analytics (heatmap/gradient map generation)

Dual-stream hybrid 3D-2D convolution neural network for breast cancer cell classification with polarization images

MuellerNet: a hybrid 3D–2D CNN for cell classification with Mueller matrix images.

This repository implements a hybrid deep learning model for exercise evaluation using the ALEX-GYM-1 dataset, leveraging two pose-understanding models—a 2D CNN architecture and a 1D CNN with residual blocks + GRU architecture. Additionally, a 3D CNN is used as a vision model to capture spatiotemporal features from video sequences.

The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

HybridSN: 3D - 2D CNN for Hyperspectral Image Classification

Comparative study of tennis stroke classification under limited data, evaluating 2D CNN, 3D CNN, pose-based LSTM, and pretrained R3D-18 models for spatial, temporal, and hybrid representation learning.

An endeavor to create a Computer Vision project to detect Schizophrenia patients among Healthy Controls from fMRI data using 2D, 3D & 4D CNNs and a hybrid 3D CN + RNN (LSTM) model.

A complete end-to-end lip-reading system that predicts text from video using a hybrid 3D CNN + 2D CNN + Bi-LSTM architecture with CTC decoding. The project includes dataset preprocessing, full training pipeline, TorchScript export, and inference with WER/CER evaluation.

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Hyperspectral image classification using a hybrid 1D/2D/3D CNN - Kaggle competition solution

This project works on implementing a light-weight CNN hybrid 3D-2D network to do binary classification on hyperspectral images.

A project exploring different CNN architectures (1D, 2D, 3D, and Hybrid) for hyperspectral image classification using the Pavia University dataset.

This project leverages 3D and hybrid 2D/3D Convolutional Neural Networks (CNNs) to classify CT scan images effectively, providing a reliable tool for early lung cancer detection.

Hybrid skin cancer detection using 2D ensemble (EfficientNetV2 + ResNet50) and synthetic 3D CNN analysis. Converts single 2D dermoscopic images into 3D volumes via monodepth augmentation. Trained on ISIC 2024 dataset for dual-path prediction: planar and volumetric insights.