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add rec data preparation in readme #35

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Mar 9, 2023
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add rec data preparation in readme #35

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c0c0582
add rec data in readme
hqkate Mar 9, 2023
da71e78
add eval datadir
hqkate Mar 9, 2023
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38 changes: 33 additions & 5 deletions README.md
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Expand Up @@ -127,23 +127,51 @@ We will use **CRNN** model and **LMDB** dataset for illustration, although other

#### 1. Data Preparation

Please download the LMDB dataset from ...
Please download the LMDB dataset from [here](https://www.dropbox.com/sh/i39abvnefllx2si/AAAbAYRvxzRp3cIE5HzqUw3ra?dl=0) (ref: [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)).

After preparation, the data structure should be like
There're several .zip data files:
- `data_lmdb_release.zip` contains the entire datasets including train, valid and evaluation.
- `validation.zip` is the union dataset for Validation
- `evaluation.zip` contains several benchmarking datasets.

Unzip the data and after preparation, the data structure should be like

``` text
.
├── train
│   ├── MJ
│   │   ├── data.mdb
│   │   ├── lock.mdb
│   ├── ST
│   │   ├── data.mdb
│   │   ├── lock.mdb
└── validation
|   ├── data.mdb
|   ├── lock.mdb
└── evaluation
├── IC03
│   ├── data.mdb
│   ├── lock.mdb
├── IC13
│   ├── data.mdb
│   ├── lock.mdb
└── ...
```

#### 2. Configure Yaml

Please choose a yaml config file containing the target pre-defined model and data pipeline that you want to re-use from `configs/det`. Here we choose `configs/det/vgg7_bilistm_ctc.yaml`.
Please choose a yaml config file containing the target pre-defined model and data pipeline that you want to re-use from `configs/rec`. Here we choose `configs/rec/vgg7_bilistm_ctc.yaml`.

Please change the data config args accordingly, such as
``` yaml
train:
dataset:
data_dir: ic15/det/train/images
label_files: ic15/det/train/det_gt.txt
type: LMDBDataset
data_dir: lmdb_data/rec/train/
eval:
dataset:
type: LMDBDataset
data_dir: lmdb_data/rec/validation/
```

Optionally, change `num_workers` according to the cores of CPU, and change `distribute` to True if you are to train in distributed mode.
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