mirrors/mmocr
1
0
Fork 0
mirror of https://github.com/open-mmlab/mmocr.git synced 2025-06-03 21:54:47 +08:00
Code Issues Projects Releases Wiki Activity

[Docs] updata abcnet doc (#1658)

Browse Source 
* updata abcnet doc

* updata link

* updata link

* updata config name

* add link for data
This commit is contained in:
liukuikun 2023-01-06 10:31:08 +08:00 committed by GitHub
parent 4d5ed98177
commit acae8da223
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 46 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

55
projects/ABCNet/README.md
View File

@ -19,9 +19,30 @@ This is an implementation of [ABCNet](https://github.com/aim-uofa/AdelaiDet) bas
<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
This ABCNet implementation works for inference only. Its full implementation will be available soon.
### Prerequisites
### Testing commands
- Python 3.7
- PyTorch 1.6 or higher
- [MIM](https://github.com/open-mmlab/mim)
- [MMOCR](https://github.com/open-mmlab/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 `ABCNet/` root directory, run the following line to add the current directory to `PYTHONPATH`:
```shell
# Linux
export PYTHONPATH=`pwd`:$PYTHONPATH
# Windows PowerShell
$env:PYTHONPATH=Get-Location
```
if the data is not in `ABCNet/`, you can link the data into `ABCNet/`:
```shell
# Linux
ln -s ${DataPath} $PYTHONPATH
# Windows PowerShell
New-Item -ItemType SymbolicLink -Path $env:PYTHONPATH -Name data -Target ${DataPath}
```
As of now, `BezierAlign` is not yet supported by MMCV, and we will use third-party MMCV with the implementation of `BezierAlign`. You will need to install it from the source code as follows:
@ -31,10 +52,26 @@ cd mmcv
MMCV_WITH_OPS=1 MAX_JOBS=8 python setup.py develop
```
In MMOCR's root directory, run the following command to test the model:
### Training commands
In the current directory, run the following command to train the model:
```bash
python tools/test.py projects/ABCNet/config/abcnet/abcnet_resnet50_fpn.py ${CHECKPOINT_PATH}
mim train mmocr config/abcnet/abcnet_resnet50_fpn_500e_icdar2015.py --work-dir work_dirs/
```
To train on multiple GPUs, e.g. 8 GPUs, run the following command:
```bash
mim train mmocr config/abcnet/abcnet_resnet50_fpn_500e_icdar2015.py --work-dir work_dirs/ --launcher pytorch --gpus 8
```
### Testing commands
In the current directory, run the following command to test the model:
```bash
mim test mmocr config/abcnet/abcnet_resnet50_fpn_500e_icdar2015.py --work-dir work_dirs/ --checkpoint ${CHECKPOINT_PATH}
```
## Results
@ -43,9 +80,9 @@ Here we provide the baseline version of ABCNet with ResNet50 backbone.
To find more variants, please visit the [official model zoo](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/BAText/README.md).
| Name | E2E-None-Hmean | det-Hmean | Download |
| :-------------------: | :------------: | :-------: | :-----------------------------------------------------------------------------------------------------------------------------------: |
| v1-icdar2015-finetune | 0.5803 | 0.8818 | [model](https://download.openmmlab.com/mmocr/textspotting/abcnet/abcnet_resnet50_fpn/abcnet_resnet50_fpn_500e_icdar2015-e8c31510.pth) |
| Name | Pretrained Model | E2E-None-Hmean | det-Hmean | Download |
| :-------------------: | :--------------------------------------------------------------------------------: | :------------: | :-------: | :------------------------------------------------------------------------: |
| v1-icdar2015-finetune | [SynthText](https://download.openmmlab.com/mmocr/textspotting/abcnet/abcnet_resnet50_fpn_500e_icdar2015/abcnet_resnet50_fpn_pretrain-d060636c.pth) | 0.6127 | 0.8753 | [model](https://download.openmmlab.com/mmocr/textspotting/abcnet/abcnet_resnet50_fpn_500e_icdar2015/abcnet_resnet50_fpn_500e_icdar2015-326ac6f4.pth) \| [log](https://download.openmmlab.com/mmocr/textspotting/abcnet/abcnet_resnet50_fpn_500e_icdar2015/20221210_170401.log) |
## Citation
@ -89,9 +126,9 @@ A project does not necessarily have to be finished in a single PR, but it's esse
<!-- As this template does. -->
- [ ] Milestone 2: Indicates a successful model implementation.
- [x] Milestone 2: Indicates a successful model implementation.
- [ ] Training-time correctness
- [x] Training-time correctness
<!-- If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range. -->