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A tool for running deep learning algorithms for semantic segmentation with satellite imagery

License: MIT License

Python 97.37% Dockerfile 2.63%
satellite-imagery deep-learning segmentation computervision nerual-network nvidia-docker encoder-decoder pixel-decoder unet labelmaker

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pixel-decoder's Issues

requirements.txt is broken

It looks like some stdout/stderr got logged to the requirement.txt file, so we're unable to install the needed deps.

Great project btw!

Problems with training.py

Hello,
I tried using pixel-decoder as you suggested in the readme file, however I am having problems with training my dataset. I use: train.train(batch_size=4, imgs_folder=imgs, masks_folder=masks, models_folder=models, model_id='resnet_unet', origin_shape_no=256, border_no=32) where imgs and masks are directories for label-maker data. However, after generating the first batch of results, I get the following ValueError: Cannot feed value of shape (4, 256, 256, 3, 1) for Tensor 'conv2d_22_target:0', which has shape '(?, ?, ?, ?)'.

From the Traceback, I believe this comes from the first instance of fit_generator, on lines 66-70. Specifically, the traceback points to line 70, the callback.

这个项目很好, 我很高兴!

Issue while Training

Loss and accuracy parameters just say NaN

loss: nan - dice_coef: nan - dice_coef_rounded: nan - binary_crossentropy: nan - val_loss: nan - val_dice_coef: nan - val_dice_coef_rounded: nan - val_binary_crossentropy: nan

screenshot from 2018-12-26 00-33-52

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