idealo / image-quality-assessment

Convolutional Neural Networks to predict the aesthetic and technical quality of images.

Home Page:https://idealo.github.io/image-quality-assessment/

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need info to build config and samples json

djangid opened this issue · comments

can anyone explain "train_env": "TID2013",
"s3_bucket": "ds-hotel-image-assessment",
"docker_image": "idealo/nima-gpu",
"base_model_name": "MobileNet",
"existing_weights": null,
"n_classes": 10,
"batch_size": 64,
"epochs_train_dense": 25,
"learning_rate_dense": 0.001,
"decay_dense": 0,
"epochs_train_all": 25,
"learning_rate_all": 0.0000003,
"decay_all": 0,
"dropout_rate": 0.75,
"multiprocessing_data_load": true,
"num_workers_data_load": 8,
"img_format": "bmp"

This config file lists all the parameters needed to train the technical model on AWS. Let us know if you have more specific questions on any of the parameters

could you please let me know how to know what field is what and more information so i can understand how to make my own filed i nfromtion

Hi, most field names are quite descriptive and should be clear if you deal with neural nets. If you have specific questions to a field please reopen this issue, but I am not going to run through all of them, closing this for now