Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

Overview

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

This repository contains the setup for all experiments performed in our Paper ... It is to be used in conjunction with the RL environment text-localization-environment, which is linked as a submodule. After cloning do git submodule init and git submodule update and follow the installation instructions of that repo.

The project is configured using Hydra in the cfg folder.

Training

We use RLLib as RL framework. Train the model by executing rllib_train.py.

Every value in the cfg folder can be altered by passing it as a CLI argument, while keeping the correct file hierarchy (e.g. data.path=/data). The folder data contains templates for different dataset configurations.

Here are explanations for a few example parameters.

Parameter Description default
neptune.offline disables logging to neptune.ai true
training.iterations how long to train 5000
training.epsilon.decay_steps length of exploration 300000
data.dataset dataset type icdar2013
data.path path to dataset /data/ICDAR2013
data.json_path path to json file of data (for SynthText) null
data.eval_path path to evaluation dataset /data/ICDAR2013
data.eval_gt_file gt zip file for IC13/IC15/TIoU eval scripts icdar13_gt.zip

Training weakly supervised:

Parameter Description
assessor.data_path path to assessor training data for on-the-fly training of the assessor
assessor.checkpoint path to assessor PyTorch (.pt) file. A pretained model can be downloaded here.

Loading a checkpoint:

Checkpoints need to be RLLib checkpoint folders. Our best three models (supervised, weakly supervised and semi-supervised) can be downloaded here.

Set the parameter restore to the checkpoint directory. Training will resume from the checkpoint. The training iterations have to be increased, as the checkpoints were made at iteration 15k.

Testing

Execute evaluate.py.

python evaluate.py 
    
     
     
       --dataset icdar2013 [--framestacking grayscale]

     
    
   

Tips

For IDE debugging change ray.init() in rllib_train.py to ray.init(local_mode=True).

Owner
Emanuel Metzenthin
Software / Data / ML Engineer, currently enrolled in M. Sc. Data Engineering at Hasso-Plattner-Institut in Potsdam.
Emanuel Metzenthin
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