Adapting the neuron network to a new architecture (Python, Tensoflow, VGG-16)
112 USD The main repository of the example
HTTPS://github.com/magenta/magenta/tree/main/magenta/models/image_stylization
In the existing example is realised on VGG-16, which is not a very effective option on mobile devices,
Communicated with the developers of the Tensorflow Gpu delegate and they wrote that my warrant was not effective and should be re-trained.It is necessary to analyze the data to scrap and rewrite.What I used
These are 3 scripts and commands with parameters for them
https://github.com/magenta/magenta/blob/main/magenta/models/image_stylization/image_stylization_create_dataset.py
https://github.com/magenta/magenta/blob/main/magenta/models/image_stylization/image_stylization_train.py
https://github.com/magenta/magenta/blob/main/magenta/models/image_stylization/image_stylization_convert_tflite.py
Python image_stylization_create_dataset.py --style_files=C:\Users\teran\PycharmProjects\magenta\arbitrary_image_stylization\data\style\*.jpg --output_file=C:\Users\teran\PycharmProjects\magenta\arbitrary_image_stylization\data\style_set\style_images.tfrecord --compute_gram_matrices=true --logtostderr
Python image_stylization_train.py --train_dir=C:\Users\teran\PycharmProjects\magenta\data\train --style_dataset_file=C:\Users\teran\PycharmProjects\magenta\data\style_set\style_images.tfrecord --num_styles=170 --alpha=0.25 --vgg_checkpoint=C:\Users\teran\PycharmProjects\magenta\data\vgg_16.ckpt --imagenet_data_dir=E:\ImageNet\output
Python image_stylization_convert_tflite.py --checkpoint=C:\Users\teran\PycharmProjects\magenta\data\train --num_styles=170 --alpha=1.00 --output_model=C:\Users\teran\PycharmProjects\magenta\data\tflite\
I’m going to add the outcome file I’m already using in the mobile app.
Workers need to be screwed to train and convert the network into tflite based on a new architecture
Please write how much time it will take and how much it will cost.
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