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Minor formatting proposals #83

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Minor formatting proposals #83

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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -26,7 +26,7 @@ If you have downloaded the previous version (testing code) of HED, please note t
### Pretrained model

We provide the pretrained model and training/testing code for the edge detection framework Holistically-Nested Edge Detection (HED). Please see the Arxiv or ICCV paper for technical details. The pretrained model (fusion-output) gives ODS=.790 and OIS=.808 result on BSDS benchmark dataset.
0. Download the pretrained model (56MB) from (http://vcl.ucsd.edu/hed/hed_pretrained_bsds.caffemodel) and place it in examples/hed/ folder.
0. Download the pretrained model (56MB) from (http://vcl.ucsd.edu/hed/hed_pretrained_bsds.caffemodel) and place it in `examples/hed/` folder.

### Installing
0. Install prerequisites for Caffe(http://caffe.berkeleyvision.org/installation.html#prequequisites)
Expand All @@ -35,11 +35,11 @@ We provide the pretrained model and training/testing code for the edge detection
### Training HED
To reproduce our results on BSDS500 dataset:
0. data: Download the augmented BSDS data (1.2GB) from (http://vcl.ucsd.edu/hed/HED-BSDS.tar) and extract it in data/ folder
0. initial model: Download fully convolutional VGG model (248MB) from (http://vcl.ucsd.edu/hed/5stage-vgg.caffemodel) and put it in examples/hed folder
0. run the python script **python solve.py** in examples/hed
0. initial model: Download fully convolutional VGG model (248MB) from (http://vcl.ucsd.edu/hed/5stage-vgg.caffemodel) and put it in `examples/hed` folder
0. run the python script **python solve.py** in `examples/hed`

### Testing HED
Please refer to the IPython Notebook in examples/hed/ to test a trained model. The fusion-output, and individual side-output from 5 scales will be produced after one forward pass.
Please refer to the IPython Notebook in `examples/hed/` to test a trained model. The fusion-output, and individual side-output from 5 scales will be produced after one forward pass.

Note that if you want to evaluate the results on BSDS benchmarking dataset, you should do the standard non-maximum suppression (NMS) and edge thinning. We used Piotr's Structured Forest matlab toolbox available here **https://github.com/pdollar/edges**. Some helper functions are also provided in the [eval/ folder](https://github.com/s9xie/hed_release-deprecated/tree/master/examples/eval).

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