Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update det dbnet README #803

Merged
merged 2 commits into from
Jan 24, 2025
Merged

Update det dbnet README #803

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
78524dd
Update det dbnet README
Jan 21, 2025
982317a
Update det dbnet README
Jan 23, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
74 changes: 4 additions & 70 deletions configs/det/dbnet/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -301,86 +301,20 @@ python tools/eval.py -c=configs/det/dbnet/db_r50_icdar15.yaml

DBNet and DBNet++ were trained on the ICDAR2015, MSRA-TD500, SCUT-CTW1500, Total-Text, and MLT2017 datasets. In addition, we conducted pre-training on the ImageNet or SynthText dataset and provided a URL to download pretrained weights. All training results are as follows:

Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode.

#### ICDAR2015


| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :------------------------------------: | :--------------------------------------------------------------------------------------------------------: |
| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 403.87 s | 65.69 | 152.23 | 74.68% | 79.38% | 76.95% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-e72f9b8b-910v2.ckpt) |
| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 403.87 s | 65.69 | 152.23 | 74.68% | 79.38% | 76.95% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-e72f9b8b-910v2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539-f14c6a13.mindir)|
| DBNet | MobileNetV3 | ImageNet | 8 | 8 | O2 | 405.35 s | 54.46 | 1175.12 | 76.27% | 76.06% | 76.17% | [yaml](db_mobilenetv3_icdar15_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-7e89e1df-910v2.ckpt) |
| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 147.81 s | 155.62 | 64.25 | 84.50% | 85.36% | 84.93% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-48153c3b-910v2.ckpt) |
| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 147.81 s | 155.62 | 64.25 | 84.50% | 85.36% | 84.93% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-48153c3b-910v2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24-fbf95c82.mindir) |
| DBNet | ResNet-50 | ImageNet | 8 | 10 | O2 | 151.23 s | 159.22 | 502.4 | 81.15% | 87.63% | 84.26% | [yaml](db_r50_icdar15_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-e10bad35-910v2.ckpt) |

> The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.

Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode.

#### ICDAR2015

| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: |:---------:| :--------: | :-----------: | :---------: | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 321.15 s | 100 | 100 | 76.31% | 78.27% | 77.28% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539-f14c6a13.mindir) |
| DBNet | MobileNetV3 | ImageNet | 8 | 8 | O2 | 309.39 s | 66.64 | 960 | 76.22% | 77.98% | 77.09% | [yaml](db_mobilenetv3_icdar15_8p.yaml) | Coming soon |
| DBNet | ResNet-18 | ImageNet | 1 | 20 | O2 | 75.23 s | 185.19 | 108 | 80.12% | 83.41% | 81.73% | [yaml](db_r18_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18-0c0c4cfa.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18-0c0c4cfa-cf46eb8b.mindir) |
| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 110.54 s | 132.98 | 75.2 | 83.53% | 86.62% | 85.05% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24-fbf95c82.mindir) |
| DBNet | ResNet-50 | ImageNet | 8 | 10 | O2 | 107.91 s | 183.92 | 435 | 82.62% | 88.54% | 85.48% | [yaml](db_r50_icdar15_8p.yaml) | Coming soon |
| DBNet++ | ResNet-50 | SynthText | 1 | 32 | O2 | 184.74 s | 409.21 | 78.2 | 86.81% | 86.85% | 86.86% | [yaml](dbpp_r50_icdar15_910.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2-e61a9c37.mindir) |

> The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.

#### MSRA-TD500


| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :-----------------------: | :---------------------------------------------------------------------------------------------: |
| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 76.18 s | 163.34 | 121.7 | 79.90% | 88.07% | 83.78% | [yaml](db_r18_td500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_td500-b5abff68.ckpt) |
| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 108.45 s | 280.90 | 71.2 | 84.02% | 87.48% | 85.71% | [yaml](db_r50_td500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_td500-0d12b5e8.ckpt) |


> MSRA-TD500 dataset has 300 training images and 200 testing images, reference paper [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947), we trained using an extra 400 traning images from HUST-TR400. You can down all [dataset](https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar) for training.

#### SCUT-CTW1500

| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
|----------------|--------------|----------------|-----------|----------------|---------------|-------------------|-------------|-----------|------------|---------------|-------------|-----------------------------|---------------------------------------------------------------------------------------------------|
| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 73.18 s | 163.80 | 122.1 | 85.68% | 85.33% | 85.50% | [yaml](db_r18_ctw1500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_ctw1500-0864b040.ckpt) |
| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 110.34 s | 180.11 | 71.4 | 87.83% | 84.71% | 86.25% | [yaml](db_r50_ctw1500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_ctw1500-f637e3d3.ckpt) |


#### Total-Text


| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :---------------------------: | :-------------------------------------------------------------------------------------------------: |
| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 77.78 s | 206.40 | 96.9 | 83.66% | 87.61% | 85.59% | [yaml](db_r18_totaltext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_totaltext-fb456ff4.ckpt) |
| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 109.15 s | 289.44 | 69.1 | 84.79% | 87.07% | 85.91% | [yaml](db_r50_totaltext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_totaltext-76d6f421.ckpt) |


#### MLT2017



| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :-------------------------: | :-----------------------------------------------------------------------------------------------: |
| DBNet | ResNet-18 | SynthText | 8 | 20 | O2 | 73.76 s | 464.00 | 344.8 | 73.62% | 83.93% | 78.44% | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
| DBNet | ResNet-50 | SynthText | 8 | 20 | O2 | 105.12 s | 523.60 | 305.6 | 76.04% | 84.51% | 80.05% | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |


#### SynthText



| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **train loss** | **recipe** | **weight** |
| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :------------: | :---------------------------: | :-------------------------------------------------------------------------------------------------: |
| DBNet | ResNet-18 | ImageNet | 1 | 16 | O2 | 78.46 s | 131.83 | 121.37 | 2.41 | [yaml](db_r18_synthtext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_synthtext-251ef3dd.ckpt) |
| DBNet | ResNet-50 | ImageNet | 1 | 16 | O2 | 108.93 s | 195.07 | 82.02 | 2.25 | [yaml](db_r50_synthtext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_synthtext-40655acb.ckpt) |


| DBNet++ | ResNet-50 | SynthText | 1 | 32 | O2 | 191.93 s | 549.24 | 58.26 | 86.81% | 86.85% | 86.86% | [yaml](dbpp_r50_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2-e61a9c37.mindir)|

### Notes
- Note that the training time of DBNet is highly affected by data processing and varies on different machines.
- The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.


## References
Expand Down
Loading
Loading