https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/
Note: this repo is not supported. License is MIT.
.. contents::
Note that labels here are 1 less than in submission file:
UNet network with batch-normalization added, training with Adam optimizer with a loss that is a sum of 0.1 cross-entropy and 0.9 dice loss. Input for UNet was a 116 by 116 pixel patch, output was 64 by 64 pixels, so there were 16 additional pixels on each side that just provided context for the prediction. Batch size was 128, learning rate was set to 0.0001 (but loss was multiplied by the batch size). Learning rate was divided by 5 on the 25-th epoch and then again by 5 on the 50-th epoch, most models were trained for 70-100 epochs. Patches that formed a batch were selected completely randomly across all images. During one epoch, network saw patches that covered about one half of the whole training set area. Best results for individual classes were achieved when training on related classes, for example buildings and structures, roads and tracks, two kinds of vehicles.
Augmentations included small rotations for some classes
(±10-25 degrees for houses, structures and both vehicle classes),
full rotations and vertical/horizontal flips
for other classes. Small amount of dropout (0.1) was used in some cases.
Alignment between channels was fixed with the help of
cv2.findTransformECC
, and lower-resolution layers were upscaled to
match RGB size. In most cases, 12 channels were used (RGB, P, M),
while in some cases just RGB and P or all 20 channels made results
slightly better.
Validation was very hard, especially for both water and both vehicle classes. In most cases, validation was performed on 5 images (6140_3_1, 6110_1_2, 6160_2_1, 6170_0_4, 6100_2_2), while other 20 were used for training. Re-training the model with the same parameters on all 25 images improved LB score.
A lot of things that either did not bring noticeable improvements, or made things worse:
Area by classs:
======== ======== ======= ======= ======== ======== ======== ======== ======= ======= ======= im_id 0 1 2 3 4 5 6 7 8 9 ======== ======== ======= ======= ======== ======== ======== ======== ======= ======= ======= 6010_1_2 0.0% 0.0653% 0.0% 1.3345% 4.5634% 0.0% 0.0% 0.0% 0.0% 0.0% 6010_4_2 0.0% 0.0% 0.0% 1.9498% 12.3410% 0.0% 0.0% 0.0% 0.0% 0.0% 6010_4_4 0.0% 0.0% 0.0% 0.0% 22.8556% 0.0% 0.0% 0.0% 0.0% 0.0% 6040_1_0 0.0% 0.0% 0.0% 1.4446% 8.0062% 0.0% 0.0% 0.0% 0.0% 0.0% 6040_1_3 0.0% 0.0% 0.0% 0.2019% 18.7376% 3.6610% 0.0% 0.0% 0.0% 0.0% 6040_2_2 0.0% 0.0% 0.0% 0.9581% 18.7348% 0.0% 0.0% 0.0% 0.0% 0.0% 6040_4_4 0.0% 0.0% 0.0% 1.8893% 2.9152% 0.0% 0.0% 0.0% 0.0% 0.0% 6060_2_3 0.1389% 0.3037% 0.0% 3.0302% 8.4519% 93.5617% 0.0% 0.0% 0.0% 0.0003% 6070_2_3 1.5524% 0.3077% 0.8135% 0.0% 16.0439% 0.0% 10.6325% 0.0543% 0.0% 0.0058% 6090_2_0 0.0% 0.0343% 0.0% 0.4072% 10.1105% 28.2399% 0.0% 0.3130% 0.0% 0.0008% 6100_1_3 8.7666% 2.7289% 2.2145% 12.2506% 6.2015% 2.6901% 0.0% 0.6839% 0.0110% 0.0459% 6100_2_2 3.1801% 0.8188% 1.1903% 3.7222% 7.6089% 44.3148% 1.8823% 0.0512% 0.0100% 0.0242% 6100_2_3 8.2184% 1.4110% 1.2099% 9.5948% 7.5323% 0.0% 0.0% 0.0603% 0.0148% 0.0661% 6110_1_2 13.1314% 2.8616% 0.4192% 4.1817% 3.3154% 49.7792% 0.0% 0.1527% 0.0% 0.0065% 6110_3_1 4.5495% 1.2561% 3.6302% 2.8221% 5.4133% 57.6089% 0.0% 0.5531% 0.0181% 0.0253% 6110_4_0 2.4051% 0.5732% 1.8409% 2.8067% 5.7379% 80.7666% 0.0% 1.4210% 0.0136% 0.0017% 6120_2_0 1.7980% 0.7257% 0.8505% 4.4026% 5.6352% 79.5910% 0.0% 0.0% 0.0138% 0.0041% 6120_2_2 20.6570% 2.0389% 4.2547% 8.6533% 4.4347% 10.2929% 0.0% 0.2859% 0.0076% 0.1560% 6140_1_2 12.9211% 2.4488% 0.3538% 4.1461% 3.1027% 49.5910% 0.0% 0.1415% 0.0% 0.0086% 6140_3_1 5.2015% 1.4349% 3.4252% 2.5189% 5.8852% 57.3959% 0.0% 0.4664% 0.0042% 0.0358% 6150_2_3 0.0% 0.6055% 0.0% 3.0197% 13.5187% 80.6649% 0.0% 0.0% 0.0% 0.0% 6160_2_1 0.0% 0.0% 0.0% 2.7986% 10.2713% 0.0% 0.0% 0.0% 0.0% 0.0% 6170_0_4 0.0% 0.0016% 0.0% 0.1994% 24.8913% 0.0% 0.0% 0.0152% 0.0% 0.0% 6170_2_4 0.0% 0.0011% 0.0% 2.5070% 7.7844% 49.5326% 0.0% 0.0089% 0.0% 0.0% 6170_4_1 0.0% 0.0% 0.0% 0.1349% 20.2214% 0.0% 0.0% 0.0% 0.0% 0.0% ======== ======== ======= ======= ======== ======== ======== ======== ======= ======= =======
Train a CNN (choose number of epochs and other hyper-params running without
--all
)::
$ ./train.py checkpoint-folder --all --hps dice_loss=10,n_epochs=70
Make submission file (check hyperparameters doing a submission for the
model trained with validation by running with --validation *value*
and optionally --valid-polygons
)::
$ ./make_submission.py checkpoint-folder submission.csv.gz
Finally, use ./merge_submission.py
to produce the final submission.
This just gives a general idea, real submissions were generated with different
hyperparameters for different classes, and all above commands have more options
that are documented in the commands themselves (use --help
, check the code
if in doubt).