Now I am working with semantic segmentation to detect the quality of apples in trees.
I have a question. How can I improve the segmentation edge.
I have checked in the jetson inference docs folder and I could see this image that I found interesting and I would like to get to that or improve.
In the case of apples, perform a retraining to a new database and not using the existing ones.
Should I carry out the labeling of images with higher resolution, and then the training?
How many images is it recommended to label for each type of apple?
What would be the recommendation to improve the output mask result??
The output resolution is related to the network architecture.
Do you retrain it with a model shared in the above link?
If yes, please share which model do you use.
The tutorial uses fcn-resnet18-voc-320x320 which indicates the output mask is 320x320.
So you will get a blurred mask when upscaling it into a standard image size, ex. 1920x1080.
In the link shared above, there is a 2048x1024 resolution model called fcn-resnet18-cityscapes-2048x1024.
Would you mind retraining the model for your use case?
Do you use jetson_inference for training or a standard PyTorch example?
Please note that the inference code you tested above is tested with the jetson_inference-based model.
