WaveBiSeNet model and peptide nanowire dataset
Description
This folder includes the WaveBiSeNet code and 3,035 labeled peptide nanowire samples for experimental use.
Files
Steps to reproduce
This dataset was created to support the training and evaluation of the WaveBiSeNet semantic segmentation model on peptide nanowire microscopy images. Image acquisition was performed using a high-resolution Transmission Electron Microscope (TEM) under controlled conditions to ensure clarity and consistency. Each original image is a high-magnification grayscale micrograph, and corresponding binary masks were generated through image binarization, resulting in a total of 3,035 image–mask pairs. The final dataset was divided into training, validation, and test sets in an approximate ratio of 7:2:1, resulting in 2,135, 600, and 300 images, respectively. Before training, all images and masks were standardized and resized using OpenCV and NumPy. The semantic segmentation model was implemented in Python 3.9.19 using PyTorch 1.8.1. Model training was conducted on an NVIDIA GeForce RTX 4090 GPU (24 GB) with CUDA 12.4. Additional libraries used in this project include torchvision, scipy, tqdm, and matplotlib.
Institutions
- Changchun Normal University