CHABCNet

ABCNet on the Chinese dataset.

Installation

git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python -m venv env
source env/bin/activate
pip install --upgrade pip

First install Detectron2 following the official guide: INSTALL.md. Then build AdelaiDet with:

pip install -U torch==1.5 torchvision==0.6 -f https://download.pytorch.org/whl/cu101/torch_stable.html 
pip install opencv-python
pip install cython pyyaml==5.1
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
pip install detectron2==0.1.3 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html
python setup.py build develop

Some projects may require special setup, please follow their own README.md in configs.

Quick Start

Inference with our trained Models

1. Select the model and config file above, for example, configs/BAText/CTW1500/attn_R_50.yaml.
2. Run the demo with

wget -O ctw1500_attn_R_50.pth https://universityofadelaide.box.com/shared/static/1bqpg9hijtn2rcooqjpffateguh9eeme.pth
python demo/demo.py \
    --config-file configs/BAText/CTW1500/attn_R_50.yaml \
    --input ./input/ \
    --output ./output/ \
    --opts MODEL.WEIGHTS ctw1500_attn_R_50.pth

or

wget -O tt_attn_R_50.pth https://cloudstor.aarnet.edu.au/plus/s/t2EFYGxNpKPUqhc/download
python demo/demo.py \
    --config-file configs/BAText/TotalText/attn_R_50.yaml \
    --input ./input/ \
    --output ./output/ \
    --opts MODEL.WEIGHTS tt_attn_R_50.pth

Train Your Own Models

1. Step one: Data processing

2. Step two: Put the ReCTS dataset to dataset folder

ReCTS:
.
├── images
│   ├── 000001.jpg
│   ├── 000002.jpg
├── annotations
│   ├── train.json

3. Step three: specify train img and annotations in adet/data/builtin.py:

Add the ReCTS dataset:

_PREDEFINED_SPLITS_TEXT = {
    "totaltext_train": ("totaltext/train_images", "totaltext/train.json"),
    "totaltext_val": ("totaltext/test_images", "totaltext/test.json"),
    "ctw1500_word_train": ("CTW1500/ctwtrain_text_image", "CTW1500/annotations/train_ctw1500_maxlen100_v2.json"),
    "ctw1500_word_test": ("CTW1500/ctwtest_text_image","CTW1500/annotations/test_ctw1500_maxlen100.json"),
    "syntext1_train": ("syntext1/images", "syntext1/annotations/train.json"),
    "syntext2_train": ("syntext2/images", "syntext2/annotations/train.json"),
    "mltbezier_word_train": ("mlt2017/images","mlt2017/annotations/train.json"),
    "ReCTS_train": ("ReCTS/train_images","ReCTS/annotations/train.json"),
    "ReCTS_test": ("ReCTS/test_images","ReCTS/annotations/test.json"),
}

4. Step four: specify train config in configs/BAText/TotalText/Base-TotalText.yaml:

Add the ReCTS dataset:

DATASETS:
  TRAIN: ("ReCTS_train",)
  TEST: ("ReCTS_test",)

5. Step five: run

OMP_NUM_THREADS=1 python tools/train_net2.py --config-file configs/BAText/CTW1500/attn_R_50.yaml --num-gpus 1

OMP_NUM_THREADS=1 python tools/train_net.py --config-file configs/BAText/TotalText/attn_R_50.yaml --num-gpus 1

OMP_NUM_THREADS=1 python tools/train_net2.py --config-file configs/BAText/Pretrain/attn_R_50.yaml --num-gpus 1