For example, we conducted tests in the laboratory, but we need to replicate it on-site where the lighting conditions may vary.
If there are significant changes in the working environment lighting, it may be necessary to retrain the deep learning model or perform model fine-tuning. This is because changes in lighting conditions can affect the appearance of images, which in turn can impact the performance and accuracy of the model.
When a model is trained in a specific environment, it learns the features and patterns of that environment. If the model is directly applied to a different environment, including variations in lighting conditions, it may struggle to make accurate predictions or identifications.
If you need to replicate the successful demo at a trade show where the lighting conditions differ from the laboratory environment, here are a few approaches:
Data augmentation: Perform augmentation techniques on the images captured in both the laboratory and trade show environments, such as adjusting brightness, contrast, etc., to simulate changes in lighting conditions. This helps the model to have better generalization capabilities.
Transfer learning: Use the model trained in the laboratory environment as a starting point and fine-tune it with a smaller amount of data from the trade show environment. This helps the model adapt to the new lighting conditions.
Retraining: If there is a significant difference between the on-site lighting and the laboratory environment, and you want the model to better adapt to the new environment, retraining the model might be a better choice.
The choice of approach depends on the extent of the environmental changes and the desired performance of the model. It’s recommended to conduct experiments and tests beforehand to determine the most suitable approach for your needs.