Which patching method is suitable and practical for training and testing deep learning networks when it comes to patching input data? Is it small patches with overlap, such as 50x50 on individual pixels, or large non-overlapping patches like 256x256?
Dear Ehsan Khankeshizadeh ,
Several image patching strategies are suitable and practical for deep learning models, depending on the specific task, dataset size, computational resources, and other factors that you are handling at a time. These could include Random Patch Extraction, Grid Patch Extraction, Sliding Window, Patch Augmentation, Patch Overlapping, and Center Patching, among others. However, based on your question:
I recommend Small Overlapping Patches (i.e., 50x50 with overlap) because they offer more data diversity, capture finer details and variations in the images, help mitigate the effects of slight translations, and enhance model robustness. Also, I'd like to point out that higher computational cost than non-overlapping patches requires careful management to avoid overfitting, especially with limited datasets.
Large Non-overlapping Patches (like 256x256) reduce the computational cost, making it more efficient for large datasets and simplifying the training process due to fewer samples and reduced augmentation complexity. Not forgetting, it may miss out on fine-grained spatial details present in smaller patches and is more sensitive to image transformations due to larger patch sizes.
I hope it helps,
Regards...
Thank you, Dear Wasswa Shafik, for your comprehensive and precise response.
What I understood from your explanation is that the choice of suitable image patching strategy depends on the specific requirements of the deep learning model and the nature of the data being processed. A trade-off between computational cost and accuracy (mitigate the effects of slight translations, and enhance model robustness) needs to be made
When deciding on the optimal image patching strategy for training and testing deep learning models, it's essential to consider various factors such as data nature, model architecture, and computational resources.
Small patches with overlap, like 50x50 with overlap, offer advantages such as increased data diversity and preservation of spatial information. However, they come with higher computational costs and potential redundancy.
On the other hand, large non-overlapping patches, such as 256x256, are computationally more efficient and reduce the risk of overfitting. Yet, they may lack detailed context and spatial information at patch boundaries.
Ultimately, the choice between these strategies involves a trade-off between computational efficiency, data diversity, and task requirements. Experimentation with different patch sizes and overlaps is often necessary to find the optimal approach for a specific use case.
- Badges
- Science topic