mmocr/projects/example_project

Dummy ResNet Wrapper

This is an example README for community projects/. We have provided detailed explanations for each field in the form of html comments, which are visible when you read the source of this README file. If you wish to submit your project to our main repository, then all the fields in this README are mandatory for others to understand what you have achieved in this implementation. For more details, read our contribution guide or approach us in Discussions.

Description

This project implements a dummy ResNet wrapper, which literally does nothing new but prints "hello world" during initialization.

Usage

Prerequisites

• Python 3.7
• PyTorch 1.6 or higher
• MIM
• MMOCR

All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In example_project/ root directory, run the following line to add the current directory to PYTHONPATH:

# Linux
export PYTHONPATH=`pwd`:$PYTHONPATH
# Windows PowerShell
$env:PYTHONPATH=Get-Location

Training commands

In MMOCR's root directory, run the following command to train the model:

mim train mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/

To train on multiple GPUs, e.g. 8 GPUs, run the following command:

mim train mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/ --launcher pytorch --gpus 8

Testing commands

In MMOCR's root directory, run the following command to test the model:

mim test mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/ --checkpoint ${CHECKPOINT_PATH}

Results

Method	Backbone	Pretrained Model	Training set	Test set	#epoch	Test size	Precision	Recall	Hmean	Download
DBNet_dummy	DummyResNet	-	ICDAR2015 Train	ICDAR2015 Test	1200	736	0.8853	0.7583	0.8169	model | log

Citation

@software{MMOCR_Contributors_OpenMMLab_Text_Detection_2020,
author = {{MMOCR Contributors}},
license = {Apache-2.0},
month = {8},
title = {{OpenMMLab Text Detection, Recognition and Understanding Toolbox}},
url = {https://github.com/open-mmlab/mmocr},
version = {0.3.0},
year = {2020}
}

Checklist

Here is a checklist illustrating a usual development workflow of a successful project, and also serves as an overview of this project's progress.

• Milestone 1: PR-ready, and acceptable to be one of the projects/.

  • Finish the code

  • Basic docstrings & proper citation

  • Test-time correctness

  • A full README

• Milestone 2: Indicates a successful model implementation.

  • Training-time correctness

• Milestone 3: Good to be a part of our core package!

  • Type hints and docstrings

  • Unit tests

  • Code polishing

  • Metafile.yml

• Move your modules into the core package following the codebase's file hierarchy structure.

• Refactor your modules into the core package following the codebase's file hierarchy structure.