How to get the most accurate result

[PapaMadeleine2022]

Hello, when I test your test.py with models/mnv3_seg/munet_mnv3_wm10.h5 and models/mnv3_seg/munet_mnv3_wm10.h5, I find the result is not very good. What model and parameters I should set to get the accurate result? Can you give some advises?

Thanks.

[anilsathyan7]

I tried a different setting for this model, refer comment: #5 (comment)
See the failure cases section for model output comparison.
You may need to increase the number of filters in the model and perhaps train with AISegment dataset for better accuracy

[anilsathyan7]

There may be couple of others reason...

1. If you compare mobilenetv2(depth multiplier 0.5) with mobilenetv3(depth` multiplier 1.0) the latter has slightly less accuracy(65.4 vs 61.9 top1).

2. There were no pretrained keras checkpoints for mobilentv3, so i had to train from scratch using the tflite model as reference.

[PapaMadeleine2022]

@anilsathyan7 Thanks for your reply. In the first reason you said mobilenetv2(depth multiplier 0.5) has more high accuracy ?

[PapaMadeleine2022]

@anilsathyan7 the models under dir models/segvid_mnv2_port256 is for the model described in Model Type - 3 ?

[anilsathyan7]

If you go to the aforementioned mobilenetv3 link, there are different version of the model.

• Only minimalistic versions can be run on android gpu(supported operations)
• The small minimilastic version has lower accuracy but is faster than mobilenetv2 dm05
• The large minimilastic version has higher accuracy but is slower than mobilenetv2 dm05

You can get the pretrained model with weights using the below code:-
mnv3=keras.applications.keras_applications.mobilenet_v3.MobileNetV3Large(input_shape=(224, 224, 3),alpha=1.0, minimalistic=True, include_top=False, weights='imagenet', backend=tf.keras.backend, layers=tf.keras.layers, models=tf.keras.models, utils=tf.keras.utils)

Now you can replace the encoder part in our model with this, connect it to the decoder and finally train the model.

Model-3 is prisma-net architecture and the models in the directory: models/segvid_mnv2_port256 are the mobinenetv2 based models with size 256.

[ldenoue]

What is the model that you have under tensorflowjs?

[kooler]

Curious about the question above as well. Tried to convert all models from h5 to TensorFlowJS, but none of them seems to perform as well as the exiting one.

[anilsathyan7]

The model under tfjs is 'deconv_fin_munet'. It's a model with 128 input isize ,inside folder models/transpose_seg.