In one of the projects with the help of python (jupyter notebook), I applied mask RCNN for masking pomegranate fruit and classify it based on its size. During model application in real time the frame rates drop due to system limitations (8 GB RAM, 500 GB SSD without GPU). Someone, please help/suggest me to built a fast model from scratch which can segment the fruit in lesser time for its online application.
Shekh Mukhtar Mansuri
One approach to building a faster model from scratch is to use a lighter network architecture, such as MobileNetV2 or EfficientNet, as the backbone for the segmentation task. These architectures are designed for speed and efficiency on mobile devices and have shown good performance on image segmentation tasks. Another option is to use transfer learning, by fine-tuning a pre-trained segmentation model on a similar task.
Additionally, you can try reducing the image resolution, so that the network has to process fewer pixels, and also try to optimize other aspects of your pipeline, such as pre-processing and post-processing steps.
Finally, you may also consider using hardware acceleration, such as using a GPU or TPU, if possible, to speed up the processing time.
Dear Sheikh Mukhtar Mansuri Reading your question, I understood that you had implemented the model in Python using Jupiter Notes. I mean this is a good tool to test part of the instruction blocks. But if you want to move to a real-time environment, Jupiter may not be the best solution. For this case, perhaps, you can use an IDE, for example Pycharm (some people prefer others) and with this tool apply test cases and test suites [Documentation at https://docs.python.org/3/library/ unittest. html]. In my opinion, my approach is to test part of the code by applying tests for each class (in object oriented programming) or function. Then, once I've tested, I move on to another function. A nice feature of unit tests and Pycharm is that with this environment you can visualize the time spent. Another bottleneck are the conversions in data structures and the transformation between CPU and GPU, so it is important to enable GPU support. Also check disk writing and disable all innecessary printing instructions. This is what usually works for me, I hope I can help you. Greetings.
As stated above, the biggest factor to improve real-time applicability would to reduce the image resolution.
Other things you could try:
- Try to reduce the number of filters and or layers used, up to the point where your segmentation/classification accurary significantly worsens.
-Quantize your model (e.g., reducing from 32-bit floating point accuracy to 8 bit
-Pruning
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