The model read all the dataset into the momory, this method is too memory consuming. Maybe it is better to read the dataset list and iterate the list when training .

Hi. I can't reproduce your result on camvid dataset. What is the learning rate and number of training epoch you used in your training, is your published result on validate or test set?.

Hi, all

When I run : th main.lua --datapath /data2/cityscapes_dataset/leftImg8bit/all_train_images/ --cachepath /data2/cityscapes_dataset/leftImg8bit/dataCache/ --dataset cs --model models/model.lua --save save_models/cityscapes/ --saveTrainConf --saveAll --plot

I got "Torch: not enough memory: you tried to allocate 17GB" error (details)

It's strange because the paper mentioned it is trained using Titan X which has 12GB memory. Why the network consumes 17GB in running?

Any suggestion to fix this issue?

Thanks!

Hi,

is there any possibility to fine-tune this model on a custom datase with different number of classes? The pre-trained weights must be exist also, as I know.

Hi,

I'm having a hell of a time trying to understand what the model is expecting in terms of input and output. I'm trying to use this model in an iOS project, so I need to convert the model to Apple's CoreML format.

Image input questions:

• For image pixel values: 0-255, 0-1, -1-1?
• RGB or BGR?
• Color bias?

Prediction output:

• Looks like the shape is # of classes, width, height?
• Predictions are positive floats from 0-100?

So far I'm having the best luck with these specifications:

import torch
from torch2coreml import convert
from torch.utils.serialization import load_lua

model = load_lua("model-cs-IoU-cpu.net")

input_shape = (3, 512, 1024)
coreml_model = convert(
        model,
        [input_shape],
        input_names=['inputImage'],
        output_names=['outputImage'],
        image_input_names=['inputImage'],
        preprocessing_args={
            'image_scale': 2/255.0
        }
    )
coreml_model.save("/home/sean/Downloads/Final/model-cs-IoU.mlmodel")

Hi, @codeAC29
I cannot obtain the high IoU in my training. I looked into your code and found that, the IoU is computed via averageValid. But this is actually computing the mean of class accuracy. The IoU should be the value of averageUnionValid. Do you notice the difference and obtain 76% IoU by averageUnionValid ?

Sorry for the trouble. For convenience, I refer the definition of averageValid and averageUnionValid here.

Hii, I am getting this error while running main.py RuntimeError: Expected object of type torch.cuda.LongTensor but found type torch.cuda.FloatTensor for argument 2 'target'. Please help me out. Also when i try to run the trained models i am running into error. I am using pytorch to run .net files. I am not able to load them as it is showing error: name cs is not defined. It is a model. Why does it have a variable named cs(here cs represents cityscapes) in it?

Hi, If we give the input image size other than 32 multiples there is a size mismatch error when adding the output from encoder3 and decoder4. For example input image size is 1000x2000 output of encoder3 is 63x125 and decoder4 output size is 64x126. We need adjust parameters for spatialfullconvolution layer only if input image size is multiple of 2^(n+1) where n is encoder depth. For other image sizes adjust parameter depends on the image size. In this example network works if adjust parameter is zero in decoders 3 and 4. Please clarify if this network works only for 2^(n+1) sizes. Thanks.

Hi, I am reproducing the LinkNet. I have a doubt about the input image resolution and the output image resolution when you compute the FLOPS. I find my FLOPS and running speed are different your results reported on your paper.

iam trying to build linknet in caffe. Could you please help me in below qns: 1)Found that there are 5 downsampling and 6 updsampling by 2. if we have different no of up sampling and down sampling(6,5) how can we get the same output shape as input. Referred:https://arxiv.org/pdf/1707.03718.pdf 2)how many iterations you ran to get the proper results. 3)To match the encoder and decoder output shape i used crop layer before Eltwise instead of adding extra row or column. Will it make any difference?

I got the camVid dataset as specified in the in the read me file and installed video-decoder

Ientered the following command to start training: th main.lua --datapath ./data/CamVid/ --cachepath ./dataCache/CamV/ --dataset cv --model ./models/model.lua --save ./Models/CamV/ --saveTrainConf --saveAll --plot

And I got the following error,

Preparing CamVid dataset for data loader Filenames and their role found in: ./misc/dataDistributionCV.txt

Getting input images and labels for: 01TP_extract.avi /home/jayp/torch/install/bin/luajit: /home/jayp/torch/install/share/lua/5.1/trepl/init.lua:389: /home/jayp/torch/install/share/lua/5.1/trepl/init.lua:389: error loading module 'libvideo_decoder' from file '/home/jayp/torch/install/lib/lua/5.1/libvideo_decoder.so': /home/jayp/torch/install/lib/lua/5.1/libvideo_decoder.so: undefined symbol: avcodec_get_frame_defaults stack traceback: [C]: in function 'error' /home/jayp/torch/install/share/lua/5.1/trepl/init.lua:389: in function 'require' main.lua:34: in main chunk [C]: in function 'dofile' ...jayp/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk

I would really appreciate if anyone would help me with this.

Thank You!