caffe-fcn | FCN for segmentation

 by   minghuam C++ Version: Current License: Non-SPDX

kandi X-RAY | caffe-fcn Summary

kandi X-RAY | caffe-fcn Summary

caffe-fcn is a C++ library. caffe-fcn has no bugs, it has no vulnerabilities and it has low support. However caffe-fcn has a Non-SPDX License. You can download it from GitHub.

This is a pre-release Caffe branch for fully convolutional networks. This includes unmerged PRs and no guarantees. Everything here is subject to change, including the history of this branch. Consider PR #2016 for reducing memory usage. See future.sh for details.
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              caffe-fcn has a low active ecosystem.
              It has 6 star(s) with 3 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 20 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of caffe-fcn is current.

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              caffe-fcn has no bugs reported.

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              caffe-fcn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              caffe-fcn has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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              caffe-fcn releases are not available. You will need to build from source code and install.

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            Community Discussions

            QUESTION

            How to format a data set for fully convolutional networks?
            Asked 2017-Dec-30 at 12:22

            I am trying to prepare my data set for fully convolutional network. I've looked through some data sets and I'm having a really hard time figuring out how to format it. For instance, in the Kitti data set, there are these 2 images and this text file in the training folder:

            image 1

            image 2

            text

            P0: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 0.000000000000e+00 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00 P1: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.875744000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00 P2: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 4.485728000000e+01 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.163791000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.745884000000e-03 P3: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.395242000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.199936000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.729905000000e-03 R0_rect: 9.999239000000e-01 9.837760000000e-03 -7.445048000000e-03 -9.869795000000e-03 9.999421000000e-01 -4.278459000000e-03 7.402527000000e-03 4.351614000000e-03 9.999631000000e-01 Tr_velo_to_cam: 7.533745000000e-03 -9.999714000000e-01 -6.166020000000e-04 -4.069766000000e-03 1.480249000000e-02 7.280733000000e-04 -9.998902000000e-01 -7.631618000000e-02 9.998621000000e-01 7.523790000000e-03 1.480755000000e-02 -2.717806000000e-01 Tr_imu_to_velo: 9.999976000000e-01 7.553071000000e-04 -2.035826000000e-03 -8.086759000000e-01 -7.854027000000e-04 9.998898000000e-01 -1.482298000000e-02 3.195559000000e-01 2.024406000000e-03 1.482454000000e-02 9.998881000000e-01 -7.997231000000e-01 Tr_cam_to_road: 9.999570839814e-01 -5.508724949246e-03 -7.452906591504e-03 9.610489538319e-03 5.425697507328e-03 9.999234779341e-01 -1.111504746388e-02 -1.597134401910e+00 7.513565886504e-03 1.107413060494e-02 9.999104059534e-01 2.788606298060e-01

            This data set is very different from the regular data sets I've seen being used for CNNs. Hence, I had the following questions:

            1. What is happening in the text file?
            2. How to generate the 2nd image with solid colored pixels?
            3. One of the proposed advantages of FCNs is the ability to feed input images of arbitrary sizes. How small can I make the input images - is 50x50 too small? I looked for some literature surrounding this but couldn't find much.

            Essentially, I'm trying to create a data set to use this network from this github. Which has only 2 folders for training: training_img_lmdb and training_label_lmdb. So, I'm not exactly sure if the text file or the pixelated image goes in the label folder. Any help would be greatly appreciated!!

            ...

            ANSWER

            Answered 2017-Dec-30 at 12:22
            1. Looks like some kind of telemetry, from Tr_cam_to_road, Tr_velo_to_cam, etc... usually the dataset will have documentation

            2. Please clarify. You posted the image. Surely you know how to load an image?

            3. You are correct, however any purely convolutional network will have a minimum input size equivalent to the input neighborhood size of a single output pixel.

            Source https://stackoverflow.com/questions/47964716

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