Reimplementation of HED based on official version of caffe
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Clone this code by
git clone https://github.com/zeakey/hed --recursive, assume your source code directory is$HED; -
Download training data from the original repo, and extract it to
$HED/data/; -
Build caffe with
bash $HED/build.sh, this will copy reimplemented loss layer to caffe folder first; -
Download initial model and put it into
$HED/model/; -
Generate network prototxts by
python model/hed.py; -
Start to train with
cd $HED && python train.py --gpu GPU-ID 2>&1 | tee hed.log.
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Download pretrained model from original repo and put it into
$HED/snapshot/; -
Generate testing network prototxt by
python $HED/model/hed.py(will generate training network prototxt as well); -
Run
cd $HED && python forward_all();
I achieved ODS=0.779 on BSDS500 dataset, which is similar to HED's 0.78. Your can train your own model and evaluate using this code.
By KAI ZHAO