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Data Data is in data/. There are three fashion datasets: fashionista-v0.2, fashionista-v1.0, and tmm_dataset_sharing. See the instruction below for data preparation.
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Models Models are in models/. There are 5 models used in fashion parsing: FCN-32s, FCN-16s, FCN-8s, Attribute Layers Training (codename: segc-8s-pre), Attribute Broadcast (codename: sege-8s), and Attribute filtering (codename: attrlog). The folder names are in - format. See the instruction below for training and running the model.
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Parsing output and evaluation result Evaluation results and symbolic links to parsing output are in /public/fashionpose. This folder will be created automatically when run the model. Evaluation results are in json format. The actual output files of Attribute Broadcast (codename: sege-8s), and Attribute filtering (codename: attrlog) model are in the model's folder.
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Script Python script and shell script are in examples/tangseng folder.
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Setup following environment: Python, Caffe, and MATLAB
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Data preparation Download and convert data into appropiate format according to README and script in each dataset's directory under data/.
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Copy fcn-32s-pascalcontext/fcn-32s-pascalcontext.caffemodel from data package to models/fcn-32s-pascalcontext/ in fashion-parsing package. This model is used as based model for training FCN-32s for fashion datasets.
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Train FCN-32s, FCN-16s, FCN-8s, Attribute Layers Training (codename: segc-8s-pre), Attribute Broadcast (codename: sege-8s), and Attribute filtering (codename: attrlog) by execute:
./examples/tangseng/train_all.sh
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Run Attribute broadcast (sege) or Attribute filtering (attrlog) network by execute:
./examples/tangseng/run_all.sh
The output will be in models/-/. h5 segmentation output and json evaluation result are expected.
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Prepare data for smoothing using CRF by execute:
./examples/tangseng/convert_h5_to_png.sh
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Compile CRF by execute:
make -C examples/tangseng/crf
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Run CRF smoothing by execute:
./examples/tangseng/run_crf.sh
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Run CRF evaluation by execute:
./examples/tangseng/crf_eval.sh
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Create symbolic links to output images and refined output images of networks by execute:
./examples/tangseng/createLinkScript.sh
The links are in public/fashionpose/ along with evaluation result in json format. Json files can be open using following command:
python -m json.tool <json_file> | less