There is a set of Pixel Level transforms that is used in the work Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

The authors also share the code => we can absorb some transforms that they have into the library.

https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

• ShotNoise
• Defocus
• GlassBlur

I trained > 100k, the loss looks fine, the bounding box looks too much, but the mask looks very wrong. Is it the visualization bug or we need more training?

Hello,

On Ubuntu 16.04, the code of tutorial imagenet-example-2.md cannot compile without adding the following line in CMakeLists.txt:

add_compile_options(-std=c++11)

When the previous line is not added, I get the following output:

-- Configuring done
-- Generating done
-- Build files have been written to: /home/akinsella/Workspace/Projects/Jetson/my-recognition
[

In file binary segmentation (camvid).ipynb, block 5, there is:

# Lets look at data we have
dataset = Dataset(x_train_dir, y_train_dir, classes=['car', 'pedestrian'])
image, mask = dataset[5] # get some sample
visualize(
    image=image, 
    cars_mask=mask[..., 0].squeeze(),
    sky_mask=mask[..., 1].squeeze(),
    background_mask=mask[..., 2].squeeze(),
)

here, sky_mask

best triplet loss ever

https://github.com/adambielski/siamese-triplet
https://github.com/andreasveit/triplet-network-pytorch
https://github.com/CoinCheung/triplet-reid-pytorch
https://discuss.pytorch.org/t/triplet-loss-in-pytorch/30634

When generating lists of tile ids covering geometries we are seeing a difference between the tile-cover and the rs cover generated tile ids. For the same geometries:

wc -l /tmp/tile-cover.tiles
116230 /tmp/tile-cover.tiles

wc -l /tmp/rs-cover.tiles 
115248 /tmp/rs-cover.tiles

Our rs cover tool does not include 982 tiles the `ti

I am not clear about what the iteration means. Could you explain more about it? Also, how is it related to epoch?

Thanks.