Datasets Comparison
Version 1
Lightweight target detection for large-field ddPCR images based on improved YOLOv5
Description
The dataset and code used in this study are crucial for advancing the accurate detection of positive microchambers in large-field ddPCR imaging. The provided dataset includes annotated ddPCR images in YOLO format, stored in the `ddpcr320/` folder. The codebase features the improved YOLOv5 model, integrating BiFPN, GhostConv, C3Ghost modules, SimAM attention mechanism, and network pruning, among other custom modifications. The `train.py` and `detect.py` scripts handle training and detection tasks, while `dataset.ipynb` demonstrates the dataset creation and splitting processes, as well as dataset processing and augmentation. The graphical user interface, developed using PyQt5 and implemented in `main_win.py`, facilitates image processing and result analysis for users. The project structure, `ddpcr_yolov5`, is systematically organized, with detailed instructions provided in the README.md file.
Institutions
Institutions
University of Shanghai for Science and Technology
Categories
Image Processing, Polymerase Chain Reaction, Automatic Target Recognition, Deep Learning
Licence
Creative Commons Attribution 4.0 International
Version 2
Lightweight target detection for large-field ddPCR images based on improved YOLOv5
Description
The dataset and code used in this study are crucial for advancing the accurate detection of positive microchambers in large-field ddPCR imaging. The provided dataset includes annotated ddPCR images in YOLO format, stored in the `ddpcr320/` folder. The codebase features the improved YOLOv5 model, integrating BiFPN, GhostConv, C3Ghost modules, SimAM attention mechanism, and network pruning, among other custom modifications. The `train.py` and `detect.py` scripts handle training and detection tasks, while `dataset.ipynb` demonstrates the dataset creation and splitting processes, as well as dataset processing and augmentation. The graphical user interface, developed using PyQt5 and implemented in `main_win.py`, facilitates image processing and result analysis for users. The project structure, `ddpcr_yolov5`, is systematically organized, with detailed instructions provided in the README.md file.
Institutions
Institutions
University of Shanghai for Science and Technology
Categories
Image Processing, Polymerase Chain Reaction, Automatic Target Recognition, Deep Learning
Licence
Creative Commons Attribution 4.0 International