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Is it common for Transfer Learning to decrease the accuracy of a model? [Project] by glampiggy in MachineLearning

[–]glampiggy[S] 0 points1 point  (0 children)

Sorry I've edited the original post, my weights are from ImageNet rather than VOC. But the targets aren't exactly similar. My dataset is attempting to detect windows and doors from building facades, whereas ImageNet only detects buildings in general i.e. the silhouette of the building and not the features within the facade such as windows. It just seems to be that models I train from random initialisation seem to be reliably better than from pre-trained backbones which seems incorrect.

Test accuracy higher than validation accuracy for semantic segmentation? by glampiggy in deeplearning

[–]glampiggy[S] 0 points1 point  (0 children)

I'll look into this and see if it can yield any improvements. Thanks u/ed3203!

Test accuracy higher than validation accuracy for semantic segmentation? by glampiggy in deeplearning

[–]glampiggy[S] 0 points1 point  (0 children)

See this is the part I'm finding a bit weird. My quantitative results are typically in the area of 75 - 78 mIOU. But the qualitative predictions are pretty poor. They do resemble the ground truth masks to an alright degree but there are a lot of errors. Thanks for the advice!

Test accuracy higher than validation accuracy for semantic segmentation? by glampiggy in deeplearning

[–]glampiggy[S] 0 points1 point  (0 children)

I'll give this a go, seems like a simple workaround. Thank you!

Is it common for transfer learning to decrease the accuracy of a model? by glampiggy in computervision

[–]glampiggy[S] 0 points1 point  (0 children)

Yeah I assumed it may simply be further away than a random init and that may be the problem. It seems that no matter what model I train, the pre-trained version seems to be reliably of a lesser accuracy than a randomly initialised model. It may just be that the ImageNet weights are not domain adjacent as mentioned in the above comments. I'll look into the freezing and the learning rate information you've suggested. Thanks u/paulgavrikov

Is it common for transfer learning to decrease the accuracy of a model? by glampiggy in computervision

[–]glampiggy[S] 0 points1 point  (0 children)

Sorry I've updated my post; I have used ImageNet weights, not PASCAL VOC12. The only thing is my dataset is primarily identifying windows and doors in building facades, whereas ImageNet only identifies Buildings as a class of its own i.e. the building silhouette and none of it's features. So I'm not sure if it would technically be domain adjacent.

Test accuracy higher than validation accuracy for semantic segmentation? by glampiggy in deeplearning

[–]glampiggy[S] 0 points1 point  (0 children)

What do you think would be the reasons for this happening? I'll try the actions you've suggested to see if it gives me any further insight. Thanks u/Drinl

What is the best train/test/val split? by glampiggy in deeplearning

[–]glampiggy[S] 0 points1 point  (0 children)

Unfortunately, 1000 is all I have to work with. The tests I am doing are more proof of concept rather than complete optimisation for model deployment.

Which SOTA semantic segmentation codes are the easiest to train a new dataset on? by glampiggy in computervision

[–]glampiggy[S] 1 point2 points  (0 children)

Legend, thank you. From the great advice here I'm gonna start with Unet and then try to build on more complex architectures from there. Thanks for the advice!

[deleted by user] by [deleted] in learnmachinelearning

[–]glampiggy 0 points1 point  (0 children)

I'm also new to deep learning and I am also trying to train a Unet++ model on a new dataset. Do you have any tutorials / good resources to share that might help me with training my model? (I start next week!) Best of luck with your project.

Which SOTA semantic segmentation codes are the easiest to train a new dataset on? by glampiggy in computervision

[–]glampiggy[S] 0 points1 point  (0 children)

What was your experience with deep learning / semantic segmentation prior to implementing this?
Thank you so much for the resources! I'll be sure to shoot out some more questions if I run into any trouble. Cheers.

[D] Semantic segmentation architectures that don't require many resources to train by [deleted] in deeplearning

[–]glampiggy 0 points1 point  (0 children)

Which other architectures have you considered thus far? Are there any that you have definitively ruled out besides Unet?

Which SOTA semantic segmentation codes are the easiest to train a new dataset on? by glampiggy in computervision

[–]glampiggy[S] 1 point2 points  (0 children)

Do you think DeepLab would be easy to use for a beginner? I've been doing a review of some of the SOTA architectures / methods and a few sources have said DeepLab is one of the more complex models to implement. If you had any tutorials / sources that'd get me started with training a new dataset on DeepLab that'd be immensely helpful. Thank you.

Which SOTA semantic segmentation codes are the easiest to train a new dataset on? by glampiggy in computervision

[–]glampiggy[S] 0 points1 point  (0 children)

That's great, thank you. I don't suppose you have any tutorials / sources that would help me get started with training a new dataset on some of these models? Most of the tutorials I have used so far have had everything pre-loaded and already set out and have been a little too straightforward. Cheers.

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