Replace Google Drive links with huggingface links

README.md

@@ -1,6 +1,6 @@
-# L-CNN — End-to-End Wireframe Parsing
+# End-to-End Wireframe Parsing
 
-This repository contains the official PyTorch implementation of the paper:  *[Yichao Zhou](https://yichaozhou.com), [Haozhi Qi](http://haozhi.io), [Yi Ma](https://people.eecs.berkeley.edu/~yima/). ["End-to-End Wireframe Parsing."](https://arxiv.org/abs/1905.03246)  ICCV 2019*.
+This repository contains the official PyTorch implementation of the paper: _[Yichao Zhou](https://yichaozhou.com), [Haozhi Qi](http://haozhi.io), [Yi Ma](https://people.eecs.berkeley.edu/~yima/). ["End-to-End Wireframe Parsing."](https://arxiv.org/abs/1905.03246) ICCV 2019_.
 
 ## Introduction
 
@@ -20,14 +20,15 @@ More random sampled results can be found in the [supplementary material](https:/
 
 The following table reports the performance metrics of several wireframe and line detectors on the [ShanghaiTech dataset](https://github.com/huangkuns/wireframe).
 
-|                                                      | ShanghaiTech (sAP<sup>10</sup>) | ShanghaiTech (AP<sup>H</sup>) | ShanghaiTech (F<sup>H</sup>) | ShanghaiTech (mAP<sup>J</sup>) | 
-| :--------------------------------------------------: | :--------------------------------: | :-----------------------------: | :----------------------------: | :------------------------------: | 
-| [LSD](https://ieeexplore.ieee.org/document/4731268/) |                 /                  |              52.0             |              61.0                |                /                 |
-|  [AFM](https://github.com/cherubicXN/afm_cvpr2019)   |                24.4                |              69.5               |              77.2              |               23.3               |
-| [Wireframe](https://github.com/huangkuns/wireframe)  |                5.1                 |              67.8               |              72.6              |               40.9               |
-|                      **L-CNN**                       |              **62.9**              |            **82.8**             |            **81.2**            |             **59.3**             |
+|                                                      | ShanghaiTech (sAP<sup>10</sup>) | ShanghaiTech (AP<sup>H</sup>) | ShanghaiTech (F<sup>H</sup>) | ShanghaiTech (mAP<sup>J</sup>) |
+| :--------------------------------------------------: | :-----------------------------: | :---------------------------: | :--------------------------: | :----------------------------: |
+| [LSD](https://ieeexplore.ieee.org/document/4731268/) |                /                |             52.0              |             61.0             |               /                |
+|  [AFM](https://github.com/cherubicXN/afm_cvpr2019)   |              24.4               |             69.5              |             77.2             |              23.3              |
+| [Wireframe](https://github.com/huangkuns/wireframe)  |               5.1               |             67.8              |             72.6             |              40.9              |
+|                      **L-CNN**                       |            **62.9**             |           **82.8**            |           **81.2**           |            **59.3**            |
 
 ### Precision-Recall Curves
+
 <p align="center">
 <img src="figs/PR-APH.svg"  width="400">
 <img src="figs/PR-sAP10.svg" width="400">
@@ -75,59 +76,61 @@ process.py                      # script for processing a dataset from a checkpo
 
 ### Installation
 
-For the ease of reproducibility, you are suggested to install [miniconda](https://docs.conda.io/en/latest/miniconda.html) before following executing the following commands. 
+For the ease of reproducibility, you are suggested to install [miniconda](https://docs.conda.io/en/latest/miniconda.html) before following executing the following commands.
 
 ```bash
 git clone https://github.com/zhou13/lcnn
 cd lcnn
 conda create -y -n lcnn
 source activate lcnn
-# Replace cudatoolkit=10.1 with your CUDA version: https://pytorch.org/
+# Modify the command with your CUDA version: https://pytorch.org/
 conda install -y pytorch cudatoolkit=10.1 -c pytorch
-conda install -y tensorboardx gdown -c conda-forge
+conda install -y tensorboardx -c conda-forge
 conda install -y pyyaml docopt matplotlib scikit-image opencv
 mkdir data logs post
 ```
 
 ### Pre-trained Models
 
-You can download our reference pre-trained models from [Google
-Drive](https://drive.google.com/file/d/1NvZkEqWNUBAfuhFPNGiCItjy4iU0UOy2).  Those models were
-trained with `config/wireframe.yaml` for 312k iterations.  Use `demo.py`, `process.py`, and
+You can download our reference pre-trained models from our [HuggingFace Repo](https://huggingface.co/yichaozhou/lcnn/tree/main/Pretrained). Those models were
+trained with `config/wireframe.yaml` for 312k iterations. Use `demo.py`, `process.py`, and
 `eval-*.py` to evaluate the pre-trained models.
 
 ### Detect Wireframes for Your Own Images
+
 To test LCNN on your own images, you need download the pre-trained models and execute
 
 ```Bash
 python ./demo.py -d 0 config/wireframe.yaml <path-to-pretrained-pth> <path-to-image>
 ```
-Here, `-d 0` is specifying the GPU ID used for evaluation, and you can specify `-d ""` to force CPU inference.
 
+Here, `-d 0` is specifying the GPU ID used for evaluation, and you can specify `-d ""` to force CPU inference.
 
 ### Downloading the Processed Dataset
+
 Make sure `curl` is installed on your system and execute
+
 ```bash
 cd data
-gdown 1T4_6Nb5r4yAXre3lf-zpmp3RbmyP1t9q -O wireframe.tar.xz
+wget https://huggingface.co/yichaozhou/lcnn/resolve/main/Data/wireframe.tar.xz
 tar xf wireframe.tar.xz
 rm wireframe.tar.xz
 cd ..
 ```
 
-If `gdown` does not work for you, you can download the pre-processed dataset
-`wireframe.tar.xz` manually from [Google
-Drive](https://drive.google.com/drive/u/1/folders/1rXLAh5VIj8jwf8vLfuZncStihRO2chFr) and proceed
+Alternatively, you can download the pre-processed dataset
+`wireframe.tar.xz` manually from our [HuggingFace Repo](https://huggingface.co/yichaozhou/lcnn/tree/main/Data) and proceed
 accordingly.
 
 #### Processing the Dataset
-*Optionally*, you can pre-process (e.g., generate heat maps, do data augmentation) the dataset from
-scratch rather than downloading the processed one.  **Skip** this section if you just want to use
+
+_Optionally_, you can pre-process (e.g., generate heat maps, do data augmentation) the dataset from
+scratch rather than downloading the processed one. **Skip** this section if you just want to use
 the pre-processed dataset `wireframe.tar.xz`.
 
 ```bash
 cd data
-gdown 1BRkqyi5CKPQF6IYzj_dQxZFQl0OwbzOf -O wireframe_raw.tar.xz
+wget https://huggingface.co/yichaozhou/lcnn/resolve/main/Data/wireframe_raw.tar.xz
 tar xf wireframe_raw.tar.xz
 rm wireframe_raw.tar.xz
 cd ..
@@ -135,14 +138,17 @@ dataset/wireframe.py data/wireframe_raw data/wireframe
 ```
 
 ### Training
+
 The default batch size assumes your have a graphics card with 12GB video memory, e.g., GTX 1080Ti or RTX 2080Ti. You may reduce the batch size if you have less video memory.
 
 To train the neural network on GPU 0 (specified by `-d 0`) with the default parameters, execute
+
 ```bash
 python ./train.py -d 0 --identifier baseline config/wireframe.yaml
 ```
 
 ## Testing Pretrained Models
+
 To generate wireframes on the validation dataset with the pretrained model, execute
 
 ```bash
@@ -152,37 +158,42 @@ To generate wireframes on the validation dataset with the pretrained model, exec
 ### Post Processing
 
 To post process the outputs from neural network (only necessary if you are going to evaluate AP<sup>H</sup>), execute
+
 ```bash
 python ./post.py --plot --thresholds="0.010,0.015" logs/RUN/npz/ITERATION post/RUN-ITERATION
 ```
-where ``--plot`` is an *optional* argument to control whether the program should also generate
+
+where `--plot` is an _optional_ argument to control whether the program should also generate
 images for visualization in addition to the npz files that contain the line information, and
-``--thresholds`` controls how aggressive the post processing is.  Multiple values in ``--thresholds``
-is convenient for hyper-parameter search.  You should  replace `RUN` and `ITERATION` to the
+`--thresholds` controls how aggressive the post processing is. Multiple values in `--thresholds`
+is convenient for hyper-parameter search. You should replace `RUN` and `ITERATION` to the
 desired value of your training instance.
 
 ### Evaluation
 
 To evaluate the sAP (recommended) of all your checkpoints under `logs/`, execute
+
 ```bash
 python eval-sAP.py logs/*/npz/*
 ```
 
 To evaluate the mAP<sup>J</sup>, execute
+
 ```bash
 python eval-mAPJ.py logs/*/npz/*
 ```
 
 To evaluate AP<sup>H</sup>, you first need to post process your result (see the previous section).
 In addition, **MATLAB is required for AP<sup>H</sup> evaluation** and `matlab` should be under your
-`$PATH`.  The **parallel computing toolbox** is highly suggested due to the usage of `parfor`.
+`$PATH`. The **parallel computing toolbox** is highly suggested due to the usage of `parfor`.
 After post processing, execute
 
 ```bash
 python eval-APH.py post/RUN-ITERATION/0_010 post/RUN-ITERATION/0_010-APH
 ```
+
 to get the plot, where `0_010` is the threshold used in the post processing, and `post/RUN-ITERATION-APH`
-is the temporary directory storing intermediate files.  Due to the usage of pixel-wise matching, 
+is the temporary directory storing intermediate files. Due to the usage of pixel-wise matching,
 the evaluation of AP<sup>H</sup> **may take up to an hour** depending on your CPUs.
 
 See the source code of `eval-sAP.py`, `eval-mAPJ.py`, `eval-APH.py`, and `misc/*.py` for more