# How to Use the Osteoclast Identifier Software
## 2 Materials and Methods  
The software was developed with YOLOv5 [10] and PyQt [11] (Ultralytics, YOLOv5, Riverbank Computing, PyQt, 2022) and created as an executable file (OC_Identifier_EN.exe of the Max Bergman Center for Biomaterials, YOLOv5 EXE version 1.0.0, 2025, [[https://opara.zih.tu-dresden.de/handle/123456789/1247](https://opara.zih.tu-dresden.de/handle/123456789/1247)]([https://opara.zih.tu-dresden.de/handle/123456789/1247](https://opara.zih.tu-dresden.de/handle/123456789/1247)), Release date 29 January 2025 and accessed on 11 March 2025). Python was used for deep learning and the GUI application [12; 13]. The software provided includes a pre-trained AI model named Demo_OC_Identifier.pt, which can be used directly for osteoclast identification. The AI training process is described elsewhere [1] and summarised here briefly.  
The AI model Demo_OC_Identifier.pt was trained using 5150 labelled images of osteoclasts and 1000 background images, collected between day 6 and day 10 of an OC culture. A two-stage training strategy was applied: firstly, an overfitting phase where identical images were used for training and validation was performed, ensuring the model captured dataset-specific features. Then, in the generalization phase, a separate validation set with new images was introduced to improve its performance on unseen data. Training was based on the YOLOv5m architecture, using pre-trained weights, an image size of 640 \(\times\) 640, and the Stochastic Gradient Descent (SGD) optimizer. The process included 300 training epochs per phase, with additional fine-tuning iterations at 350, 500, and 600 epochs. Standard YOLOv5 data augmentation techniques--such as mosaic augmentation, HSV colour transformation, random cropping, scale jittering, and horizontal flipping--were applied to enhance robustness. Training followed YOLOv5's standard workflow, using train.py for initial training and train_continue.py for iterative refinements. Regular evaluations, including confusion matrices and precision-recall curves, guided the training progress.  
The minimum system requirements for the software are Windows 10, Intel i5 and 8 GB RAM (CPU only) (Intel Corporation, Santa Clara, CA, USA), whereas a GPU RTX 3060 (NVIDIA Corporation, Santa Clara, CA, USA), 16 GB RAM and CUDA 11.8 is recommended. In addition, at least 10 GB of free hard drive space should be available (the software itself requires approx. 4.6 GB, plus space for runtime and results files). The software was tested with these parameters and used successfully in routine operation. The AI model for osteoclast identification is to be regarded as a trial model and is undergoing continuous further development.  
To use the software, unzip it after downloading. The folder contains the following files and directories: _internal/\(\rightarrow\)Contains internal system files for the software. **uil/\(\rightarrow\)**Contains the user interface files. **classes.txt**\(\rightarrow\)Provides a list of classes for object recognition. **OC_Identifier_EN.exe**[14]\(\rightarrow\)Provides the executable file for using the software (please ensure the integrity of these documents). After starting the software for the first time, the following files and folders are also created: **debug.log**\(\rightarrow\)Contains logs for error analysis. **runs/\(\rightarrow\)Saves the results of the image analysis, including recognised objects and associated files. (You can delete them as required).