Training on SUN RGB-D dataset

[Nestarneal]

Hi,

I want to train ENet model on SUN RGB-D dataset, but I found that the ground truth of each image is not consistent.

I following the source code to load the label of each image with m = require 'matio' label = m.load(/path/to/folders/'seg.mat').seglabel Then, drawing an output image with the label, and making different index label has different color.

But, for example, beds are labelled with different color/index in following images

And other objects have different index in different images. Also, SUN RGB-D dataset has 38 classes (including unlabelled class), so the index interval should be [0, 37] or [1, 38]. But some seg.mat file has the index number larger than 37 and 38, for example, 45, 46 appeared.

I'm wonder what's going wrong about the ground truths?

Many thanks.

[Nestarneal]

Hey, I found the answer. Closing the issue.

[xiaofanglegoc]

@Nestarneal could you please give more details on how you produce the results on SUNRGBD ? I try to train the ENet on the SUNRGBD also. But I have encountered following bugs: 1, some images do not exist in the file sunImgPath.tsv (So I skip those images); 2, values in some images go to 143, while they are supposed in [0 38]. Thank you very much

[Nestarneal]

@xiaofanglegoc , I have no idea about your first problem because I didn't encounter it. And for the second problem, you can try m = require 'matio' m.use_lua_strings = true gt = m.load('/path/to/seg.mat') local_label = gt.seglabel local_table = gt.names The indexes in local_label in based on local_table, so you need to convert the indexes into global indexes before training.

[xiaofanglegoc]

Thank you very much, I have solved my problem

On 22 Dec 2016, at 08:15, Nestarneal notifications@github.com wrote:

@xiaofanglegoc https://github.com/xiaofanglegoc , I have no idea about your first problem because I didn't encounter it. And for the second problem, you can try m = require 'matio' m.use_lua_strings = true gt = m.load('/path/to/seg.mat') local_label = gt.seglabel local_table = gt.names The indexes in local_label in based on local_table, so you need to convert the indexes into global indexes before training.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/e-lab/ENet-training/issues/37#issuecomment-268734896, or mute the thread https://github.com/notifications/unsubscribe-auth/AGWgnmpqgQ37E6mEPVyj2lQk_wvBjTSVks5rKiOdgaJpZM4LIjSj.

[rantengsky]

how to convert the indexes into global indexes?

[guy4261]

@Nestarneal

In the SUNRGBDtoolbox.zip, under Metadata, there's a file containing all the segmentations. The values are in [0, 37] (aka both are included) - so there is one extra class for "no-class". But is it 0 or 37? Did you find this out?

Thanks!