waterquality
Required:
python>=3.6
pytorch>=1.2
Keras=2.2
tensorflow=1.14
Data available online here , in npy format.
Index:
image-image7/: raw image folder (deprecated; please using .npy file for analysis). name: XXX nb_batch a/b.jpg XXX means content value, nb_batch means batch number of that image. (total nb of batch equals to nb of folders). a/b means testing under bottle a or b
classic_classification/, classic_regression/: results showing. subfolder "Gray" have some fun images
classification_result/: conventional methods comparsion experiment, in .py file
interval_result/: results by interval experiments
modelWeights/: h5 files of nn weight
result/: csv format, comparsion table, for different model settings
result_analyse/: measure how many predicted points in each bin(interval)
water/: playground for the data and model middle layer. (feat_analysis.ipynb)
website/: oil prediction website.
pytorch-cifar/:
main.py: wrapper for training models/: conventional models and new models (mainly attnResNet50.py)
/bin_analyse.py
comparsion experiments. such as test_different_pretrain_model_with_gen()
/classic_classification.py /classic_regerssion.py /classification.py comparsion experiments with different conventional methods. treated as classification / regression
/CNN_regression.py /CNN_regression2.py /CNN.py old models
/combined_trick_model.py pretrained resnet18 with linear fc
/data_loader.py load image dataset,image hight/width...
/data_to_npy.py save the processed data as .npy,
/draw_width.py draw predicted bin width
/feature_test.ipynb feature engineering, middle layer visualization, histogram analyse
/interval_process.py calculate interval array and store
/model_eval.py comparsion test different pretrain model
/multiNets.py /multiNetsVgg.py old model used in server, plain DNN/VGG
/ordinal_categorical_crossentropy.py loss function in Keras, for ordinal loss
/plot_result_fig.py scatter plot for prediction results
/predictioin_tocsv.py show_roc_pr_curve, store comparsion result in a .csv
Please cite
@misc{waterquality,
author = {minoriwww},
title = {waterquality project & OilSS},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/minoriwww/waterquality}}
}