IuliuNovac
commented
11 months ago It's a great idea, one thing we can start with is decrease the encoder size. Right now, the longest side must be 1024. We should be able to have a smaller one.
I can tell you, that it's most likely the current developers won't implement this features, as it's a more a display of results of their scientific work.
Dear MobileSAM Developers,
I hope this message finds you well. I am reaching out to discuss potential enhancements to the MobileSAM framework, particularly concerning its lightweight encoder's performance in real-time applications.
While the current iteration of MobileSAM has made significant strides in reducing model size and accelerating inference times, I believe there is an opportunity to further optimise the encoder for real-time processing on mobile devices. Real-time segmentation is crucial for applications such as augmented reality (AR), live video effects, and interactive gaming, where latency can greatly affect user experience.
To this end, I propose the following enhancements:
Model Quantisation: Implementing post-training quantisation techniques to reduce the precision of the model's parameters, which can lead to smaller model sizes and faster inference without a substantial decrease in accuracy.
Knowledge Distillation: Exploring knowledge distillation methods where a compact student model is trained to emulate the behaviour of a larger, more powerful teacher model. This could potentially improve the real-time performance of MobileSAM without compromising the quality of segmentation.
Hardware-Accelerated Inference: Leveraging hardware acceleration options available on modern mobile devices, such as GPU and DSP, to further speed up the inference time of MobileSAM's encoder.
Network Pruning: Investigating structured and unstructured pruning methods to remove redundant parameters from the encoder, which can lead to a more efficient model that retains most of its predictive power.
Adaptive Inference: Developing an adaptive inference mechanism that dynamically adjusts the complexity of the model based on the current computational budget or real-time performance requirements.
I am eager to hear your thoughts on these suggestions and to discuss how we might collaborate to implement these enhancements. I believe that by addressing these areas, we can make MobileSAM an even more attractive solution for developers looking to integrate advanced segmentation capabilities into their real-time mobile applications.
Thank you for your time and consideration. I look forward to the possibility of contributing to the MobileSAM project.
Best regards, yihong