# OCDNet Model Overview

## Model Overview <a class="anchor" name="model_overview"></a>

The model described in this card is an optical characters detection network, which aims to detect text in images. Trainable and deployable OCDNet models are provided. These are trained on Uber-Text dataset and ICDAR2015 dataset respectively.

## Terms of use <a class="anchor" name="terms_of_use"></a>
License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).

## Model Architecture <a class="anchor" name="model_architecture"></a>

This model is based on a relatively sophisticated text detection network called DBNet. DBNet is a network architecture for real-time scene text detection with differentiable binarization. It aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes.

## Training <a class="anchor" name="training_algorithm"></a>

The training algorithm inserts the binarization operation into the segmentation network and jointly optimizes it so that
the network can learn to separate foreground and background pixels more effectively. The binarization threshold is learned by minimizing the IoU loss between the predicted binary map and the ground truth binary map.


### Training Data <a class="anchor" name="training_data"></a>

The trainable models were trained on the [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html) dataset and [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) dataset respectively. The Uber-Text dataset contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts--including train_4Kx4K, train_1Kx1K, val_4Kx4K, val_1Kx1K, test_4Kx4K as the training datasets and test_1Kx1K as the validation dataset. The dataset was constructed with 107812 images for training and 10157 images for validation. The ICDAR2015 dataset contains 1000 training images and 500 test images. The deployable models were ONNX models that were exported using the trainable models.

## Performance <a class="anchor" name="performance"></a>

### Evaluation Data <a class="anchor" name="evaluation_data"></a>

The OCDNet model was evaluated using the Uber-Text test dataset and ICDAR2015 test dataset.

### Methodology and KPI

The key performance indicator is the hmean of detection. The KPI for the evaluation data are reported below.

|model|test dataset|hmean|
|---|---|---|
|ocdnet_deformable_resnet18|Uber-Text|81.1%|
|ocdnet_deformable_resnet50|Uber-Text|82.2%|
|ocdnet_fan_tiny_2x_ubertext.pth|Uber-Text|86.0%|
|ocdnet_fan_tiny_2x_icdar.pth|ICDAR2015|85.3%|
|ocdnet_fan_tiny_2x_icdar_pruned.pth|ICDAR2015|84.8%|
|ocdnet_vit_pcb.pth|Internal PCB validation|69.3%|


### Real-time Inference Performance <a class="anchor" name="realtime_inference_performance"></a>

The inference uses FP16 precision. The input shape is `<batch>x3x640x640`. The inference performance runs against an OCDNet-deployable model with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on AGX Orin, Orin NX, Orin Nano, NVIDIA L4, NVIDIA L4, and NVIDIA A100 GPUs. The Jetson devices run at Max-N configuration for maximum system performance. The data is for inference-only performance. The end-to-end performance with streaming video data might vary slightly depending on the application's use case.

|Model|Device|precision|batch_size|FPS|
|---|---|---|---|---|
|ocdnet_deformable_resnet18|Orin Nano|FP16|32|31|
|ocdnet_deformable_resnet18|Orin NX|FP16|32|46|
|ocdnet_deformable_resnet18|AGX Orin|FP16|32|122|
|ocdnet_deformable_resnet18|T4|FP16|32|294|
|ocdnet_deformable_resnet18|L4|FP16|32|432|
|ocdnet_deformable_resnet18|A100|FP16|32|1786|
|ocdnet_fan_tiny_2x_icdar|Orin Nano|FP16|1|0.57|
|ocdnet_fan_tiny_2x_icdar|AGX Orin|FP16|1|2.24|
|ocdnet_fan_tiny_2x_icdar|T4|FP16|1|2.74|
|ocdnet_fan_tiny_2x_icdar|L4|FP16|1|5.36|
|ocdnet_fan_tiny_2x_icdar|A30|FP16|1|8.34|
|ocdnet_fan_tiny_2x_icdar|L40|FP16|1|15.01|
|ocdnet_fan_tiny_2x_icdar|A100-sxm4-80gb|FP16|1|16.61|
|ocdnet_fan_tiny_2x_icdar|H100-sxm-80gb-hbm3|FP16|1|29.13|
|ocdnet_fan_tiny_2x_icdar_pruned|Orin Nano|FP16|2|0.79|
|ocdnet_fan_tiny_2x_icdar_pruned|Orin NX|FP16|2|1.18|
|ocdnet_fan_tiny_2x_icdar_pruned|AGX Orin|FP16|2|3.08|
|ocdnet_fan_tiny_2x_icdar_pruned|A2|FP16|1|2.30|
|ocdnet_fan_tiny_2x_icdar_pruned|T4|FP16|2|3.51|
|ocdnet_fan_tiny_2x_icdar_pruned|L4|FP16|1|7.23|
|ocdnet_fan_tiny_2x_icdar_pruned|A30|FP16|2|11.37|
|ocdnet_fan_tiny_2x_icdar_pruned|L40|FP16|2|19.04|
|ocdnet_fan_tiny_2x_icdar_pruned|A100-sxm4-80gb|FP16|2|22.66|
|ocdnet_fan_tiny_2x_icdar_pruned|H100-sxm-80gb-hbm3|FP16|2|40.07|


## How to Use This Model <a class="anchor" name="how_to_use_this_model"></a>

This model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices, with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).

The primary use case for this model is to detect text on images.

There are two types of models provided (both unpruned).

- trainable
- deployable

The `trainable` models are intended for training with the user's own dataset using TAO Toolkit. This can provide high-fidelity models that are adapted to the use case. A Jupyter notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit) and can be used to re-train.

The `deployable` models share the same structure as the `trainable` model, but in `onnx` format. The `deployable` models can be deployed using TensorRT, nvOCDR, and DeepStream.


### Input

Images of C x H x W  (H and W should be multiples of 32.)

### Output

BBox or polygon coordinates for each detected text in the input image

### Instructions to Use the Model with TAO

To use these models as pretrained weights for transfer learning, use the snippet below as a template for the `model` component of the experiment spec file to train an OCDNet model. For more information on the experiment spec file, refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).

To use trainable_resnet18_v1.0 model:
```yaml
model:
  load_pruned_graph: False
  pruned_graph_path: '/results/prune/pruned_0.1.pth'
  pretrained_model_path: '/data/ocdnet/ocdnet_deformable_resnet18.pth'
  backbone: deformable_resnet18
```
To use trainable_ocdnet_vit_v1.0 model:
```yaml
model:
  load_pruned_graph: False
  pruned_graph_path: '/results/prune/pruned_0.1.pth'
  pretrained_model_path: '/data/ocdnet/ocdnet_fan_tiny_2x_icdar.pth'
  backbone: fan_tiny_8_p4_hybrid
  enlarge_feature_map_size: True
  activation_checkpoint: True
```

### Instructions to deploy the model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the Deepstream sample app.

To deploy this model with [DeepStream](https://developer.nvidia.com/deepstream-sdk), follow [these instructions](https://docs.nvidia.com/tao/tao-toolkit/text/ds_tao/nvocdr_ds.html).

## Limitations <a class="anchor" name="limitations"></a>

### Restricted Usage in Different Fields

The NVIDIA OCDNet trainable model is trained on Uber Text, ICDAR2015 and PCB text dataset, which contains street-view images only. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.

## Model versions:

- **trainable_resnet18_v1.0** - Pre-trained models with deformable-resnet18 backbone, trained on Uber-Text dataset.
- **trainable_resnet50_v1.0** - Pre-trained models with deformable-resnet50 backbone, trained on Uber-Text dataset.
- **trainable_ocdnet_vit_v1.0** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset.
- **trainable_ocdnet_vit_v1.1** - Pre-trained models with fan-tiny backbone, trained on Uber-Text dataset.
- **trainable_ocdnet_vit_v1.2** - Pre-trained models with fan-tiny backbone, trained on PCB dataset.
- **trainable_ocdnet_vit_v1.3** - Pre-trained models with fan-tiny backbone, trained on ImageNet2012 dataset.
- **trainable_ocdnet_vit_v1.4** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset and model are pruned.
- **deployable_v1.0** - Model deployable with deformable-resnet backbone.
- **deployable_v2.0** - Model deployable with fan-tiny backbone, trained on ICDAR2015.
- **deployable_v2.1** - Model deployable with fan-tiny backbone, trained on Uber-Text.
- **deployable_v2.2** - Model deployable with fan-tiny backbone, trained on PCB dataset.
- **deployable_v2.3** - Model deployable with fan-tiny backbone, trained on ICDAR2015 and model are pruned.

## Reference

### Citations <a class="anchor" name="citations"></a>

- Liao M., Wan Z., Yao C., Chen K., Bai X.: Real-time Scene Text Detection with Differentiable Binarization (2020).
- Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y: Deformable convolutional networks. (2017).
- He, W., Zhang, X., Yin, F., and Liu, C.: Deep direct regression for multi-oriented scene text detection. (2017).
- Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).
- Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.
- Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.

## Using TAO Pre-trained Models <a class="anchor" name="using_tlt_pretrained_models"></a>

- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)
- Get other purpose-built models from the NGC model registry:
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  - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)
  - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)
  - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)
  - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)
  - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)
  - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)
  - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)
  - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)
  - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)
  - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)
  - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)
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  - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)
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  - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)
  - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)
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  - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)

## License <a class="anchor" name="license"></a>

The license to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).

## Technical Blogs <a class="anchor" name="technical_blogs"></a>

- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)
- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)
- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)
- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)
- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)
- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1)  |  [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)
- Learn how to traina  [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.
- Model accuracy is extremely important. Learn how to achieve [state-of-the-art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO.


## Suggested Reading <a class="anchor" name="suggested_reading"></a>

- More information about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit).
- Read the [TAO Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).
- If you have any questions or feedback, please refer to the discussions on the [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17).
- Deploy your model on the edge using the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).

## Ethical AI <a class="anchor" name="ethical_ai"></a>

The NVIDIA OCDNet model detects optical characters.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability)

ocdnet

Network to detect characters in an image.