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MixNet Overview

Model Overview

Description

MixNet is an optical character detection model that aims to detect text in images. It is a deep learning model designed for accurate detection of challenging scene text in natural images, particularly focusing on small and irregularly positioned text under diverse lighting and orientations. This model is ready for commercial/non-commercial use.

License/Terms of Use

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Deployment Geography

Global

Use Case

This model can be used in any computer vision application, which aims to detect text characters in images.

Release Date

NGC [06/30/2025]

Model Architecture

Architecture Type: Convolution Neural Network + Transformer Block.\nNetwork Architecture:\nThis model was developed based on the MixNet. MixNet is a cutting-edge model for scene text detection, notable for its hybrid CNN-Transformer design and strong benchmark performance. Main features include FSNet(Feature Shuffle Network) and CTBlock(Central Transformer Block). FSNet serves as the backbone, introducing a novel feature shuffling strategy to exchange features across multiple scales. CTBlock exploits the 1D manifold constraint of scene text by focusing on center line features, which helps in distinguishing closely located small text better than contour-based methods.

Input

Input Type: Image
Input Formats: Red, Green, Blue (RGB)
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: Width and height are multiples of 32.

Output

Input Type: Image
Input Formats: Red, Green, Blue (RGB)
Output Parameters: Two-Dimensional (2D)
Other Properties Related to Output: Spatial maps or coordinates indicating detected text regions.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

TAO 5.5.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Volta

[Preferred/Supported] Operating System(s):

Linux

Model versions:

deployable_v1.0 - Models deployable with MixNet.

Training and Evaluation Datasets

The total size: 77K images
Total number of datasets: 3 training datasets, 2 evaluation datasets
Dataset partition: training and evaluation are different datasets

Training Datasets

Link:

Data Collection Method by dataset:

Hybrid: Automated, Synthetic, Human

Labeling Method by dataset:

Hybrid: Automated, Synthetic

Properties:
\nThe MixNet pretrained model was trained on three datasets, totaling about 77K images.\nOne is ICDAR2015 training dataset, we oversample it to 20K images.\nAnother is Uber-Text dataset. We fiter the train_4Kx4K and train_1Kx1K datasets to make sure each image has word-only text instead of sentence text. We get 29,992 images from train_4Kx4K dataset and 12,312 images from train_1Kx1K dataset.\nThe third dataset is a synthetic dataset based on the SynthText background images. They are official background images which every user can generate synthetic texts on them. We generate 7,674 images which have large texts and 7,331 images which have small single texts.

Evaluation Datasets

Link:

ICDAR15
Uber-Text

Data Collection Method by dataset:

Hybrid: Automated, Synthetic, Human

Labeling Method by dataset:

Hybrid: Automated, Synthetic

Properties:
\nWe evaluate the MixNet model on two different datasets: word-only images from Uber-Text 1Kx1K dataset, ICDAR15 test dataset.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

dataset	eval image numbers
ICDAR15	500
Uber-Text	7461

Performance

Methodology and KPI

In text characters detection models, the F1-score is a key performance metric that measures how well the model balances precision (the proportion of correct positive detections among all positive detections) and recall (the proportion of actual positives that are correctly detected). The KPI for the evaluation data are reported below.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

model	dataset	F1-score
MixNet	ICDAR15	86.5%
MixNet	Uber-Text	88.0%
ocdnet-vit	ICDAR15	85.3%
ocdnet-vit	Uber-Text	86.0%
ocdnet_deformable_resnet50	Uber-Text	82.2%
ocdnet_deformable_resnet18	Uber-Text	81.1%

Inference

Acceleration Engine: TensorRT

Test Hardware:

The inference uses FP16 precision. The inference performance runs against a deployable model with trtexec on NVIDIA A40 GPU. 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.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

model	gpu	precision	input-size	Batch-size	FPS
MixNet	A40	FP16	1024x1024	1	52
MixNet	A40	FP16	960x960	1	59

To find more performance data about other kinds of detection models, please refer to ocdnet.

How to use this model

This model is suggested to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. For software, this model can run with NVIDIA-Optical-Character-Detection-and-Recognition-Solution or onnxruntime.

There are two types of models provided:

deployable (unpruned)

The deployable model is in onnx format. The deployable models can be deployed in TensorRT and nvOCDR, or onnxruntime.

Instructions to use the model with nvOCDR

Please refer to the C++ Sample.

Limitations

Restricted usage in different fields:

The model was trained on the ICDAR2015, Uber-text, and augmented SynthText datasets. Its generalization performance may be inadequate in scenarios that differ significantly from the training data, such as images containing numerous small text elements on PCB boards. In these cases, the model may struggle to accurately detect text. To address this limitation, augmenting the training dataset with PCB-specific images can enhance the model's ability to generalize to such challenging scenarios. Generally, achieving a better F1-score in a specific domain requires more data.

Reference

Citations

Zeng, Yu-Xiang, et al. \"MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild.\" arXiv preprint arXiv:2308.12817, 2023.

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Technical blogs

Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO
Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
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
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

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It is a deep learning model designed for accurate detection of challenging scene text in natural images, particularly focusing on small and irregularly positioned text under diverse lighting and orientations. This model is ready for commercial/non-commercial use.\n\n## License/Terms of Use\n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Deployment Geography\n\nGlobal\n\n## Use Case\n\nThis model can be used in any computer vision application, which aims to detect text characters in images.\n\n## Release Date\n\nNGC [06/30/2025]\n\n## Model Architecture \n\n**Architecture Type:** Convolution Neural Network + Transformer Block.\n**Network Architecture:**\nThis model was developed based on the [MixNet](https://github.com/D641593/MixNet). MixNet is a cutting-edge model for scene text detection, notable for its hybrid CNN-Transformer design and strong benchmark performance. Main features include FSNet(Feature Shuffle Network) and CTBlock(Central Transformer Block). FSNet serves as the backbone, introducing a novel feature shuffling strategy to exchange features across multiple scales. CTBlock exploits the 1D manifold constraint of scene text by focusing on center line features, which helps in distinguishing closely located small text better than contour-based methods. \n\n### Input\n- **Input Type:** Image\n- **Input Formats:** Red, Green, Blue (RGB)\n- **Input Parameters:** Two-Dimensional (2D)\n- **Other Properties Related to Input:** Width and height are multiples of 32.\n\n### Output\n- **Input Type:** Image\n- **Input Formats:** Red, Green, Blue (RGB)\n- **Output Parameters:** Two-Dimensional (2D)\n- **Other Properties Related to Output:** Spatial maps or coordinates indicating detected text regions.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration:\n\n### Runtime Engine(s):\n- TAO 5.5.0\n\n### Supported Hardware Microarchitecture Compatibility:\n- NVIDIA Ampere\n- NVIDIA Jetson \n- NVIDIA Hopper \n- NVIDIA Volta \n\n### [Preferred/Supported] Operating System(s):\n- Linux\n\n## Model versions:\n\n- **deployable_v1.0** - Models deployable with MixNet.\n\n## Training and Evaluation Datasets\n\n- **The total size:** 77K images\n- **Total number of datasets:** 3 training datasets, 2 evaluation datasets\n- **Dataset partition:** training and evaluation are different datasets\n\n### Training Datasets\n**Link:** \n* [ICDAR15](https://rrc.cvc.uab.es/?ch=4)\n* [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html)\n* [Synthetic-dataset](https://github.com/ankush-me/SynthText#pre-processed-background-images)\n\n**Data Collection Method by dataset:**
\n* Hybrid: Automated, Synthetic, Human
\n\n**Labeling Method by dataset:**
\n* Hybrid: Automated, Synthetic
\n\n**Properties:**
\nThe MixNet pretrained model was trained on three datasets, totaling about 77K images.\nOne is ICDAR2015 training dataset, we oversample it to 20K images.\nAnother is Uber-Text dataset. We fiter the train_4Kx4K and train_1Kx1K datasets to make sure each image has word-only text instead of sentence text. We get 29,992 images from train_4Kx4K dataset and 12,312 images from train_1Kx1K dataset.\nThe third dataset is a synthetic dataset based on the [SynthText background images](https://github.com/ankush-me/SynthText#pre-processed-background-images). They are official background images which every user can generate synthetic texts on them. We generate 7,674 images which have large texts and 7,331 images which have small single texts.\n\n\n### Evaluation Datasets\n\n**Link:** \n* [ICDAR15](https://rrc.cvc.uab.es/?ch=4)\n* [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html)\n\n**Data Collection Method by dataset:**
\n* Hybrid: Automated, Synthetic, Human
\n\n**Labeling Method by dataset:**
\n* Hybrid: Automated, Synthetic
\n\n**Properties:**
\nWe evaluate the MixNet model on two different datasets: word-only images from Uber-Text 1Kx1K dataset, ICDAR15 test dataset.\n\n |dataset|eval image numbers\n |:---:|:---:|\n |ICDAR15|500\n |Uber-Text|7461\n\n\n## Performance \n\n### Methodology and KPI\n\nIn text characters detection models, the F1-score is a key performance metric that measures how well the model balances precision (the proportion of correct positive detections among all positive detections) and recall (the proportion of actual positives that are correctly detected). The KPI for the evaluation data are reported below.\n\n|model|dataset|F1-score|\n|---|---|---|\n|MixNet|ICDAR15|86.5%|\n|MixNet|Uber-Text|88.0%|\n|ocdnet-vit|ICDAR15|85.3%|\n|ocdnet-vit|Uber-Text|86.0%|\n|ocdnet_deformable_resnet50|Uber-Text|82.2%|\n|ocdnet_deformable_resnet18|Uber-Text|81.1%|\n\n### Inference\n\n**Acceleration Engine:** TensorRT
\n\n**Test Hardware:**
\n- A40\n\nThe inference uses FP16 precision. The inference performance runs against a deployable model with trtexec on NVIDIA A40 GPU. 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.\n\n|model|gpu|precision|input-size|Batch-size|FPS|\n|---|---|---|---|---|---|\n|MixNet|A40|FP16|1024x1024|1|52|\n|MixNet|A40|FP16|960x960|1|59|\n\nTo find more performance data about other kinds of detection models, please refer to [ocdnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet).\n\n\n## How to use this model \n\nThis model is suggested to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. For software, this model can run with [NVIDIA-Optical-Character-Detection-and-Recognition-Solution](https://github.com/NVIDIA-AI-IOT/NVIDIA-Optical-Character-Detection-and-Recognition-Solution) or onnxruntime.\n\nThere are two types of models provided:\n- deployable (unpruned)\n\nThe `deployable` model is in `onnx` format. The `deployable` models can be deployed in TensorRT and nvOCDR, or onnxruntime.\n\n\n### Instructions to use the model with nvOCDR\n\nPlease refer to the [C++ Sample](https://github.com/NVIDIA-AI-IOT/NVIDIA-Optical-Character-Detection-and-Recognition-Solution/tree/tao-5.5-ocr-v3).\n\n## Limitations \n\n### Restricted usage in different fields:\nThe model was trained on the ICDAR2015, Uber-text, and augmented SynthText datasets. Its generalization performance may be inadequate in scenarios that differ significantly from the training data, such as images containing numerous small text elements on PCB boards. In these cases, the model may struggle to accurately detect text. To address this limitation, augmenting the training dataset with PCB-specific images can enhance the model's ability to generalize to such challenging scenarios. Generally, achieving a better F1-score in a specific domain requires more data.\n\n## Reference \n\n### Citations \n\n- Zeng, Yu-Xiang, et al. \"MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild.\" arXiv preprint arXiv:2308.12817, 2023.\n\n## Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n## Technical blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [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/)\n- [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/)\n- [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/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [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/)\n- 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)\n- Learn how to train [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.\n- Model accuracy is extremely important, learn how you can 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\n\n\n## Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html). \n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about DeepStream SDK https://developer.nvidia.com/deepstream-sdk\n\n## Ethical Considerations \n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","MixNet",["D",1754419701330],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/mixnet","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[236,237,238,239,240,241,242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_204":266,"_244":267,"_245":268,"_247":248},{"_204":263,"_244":263,"_245":264,"_247":248},{"_204":261,"_244":261,"_245":262,"_247":248},{"_204":259,"_244":259,"_245":260,"_247":248},{"_204":255,"_244":256,"_245":257,"_247":248},{"_204":251,"_244":252,"_245":253,"_247":248},{"_204":243,"_244":243,"_245":246,"_247":248},"TAO Toolkit","value","filter",{"_249":250,"_244":243},"category","label","field","framework","Smart Cities / Spaces","indus_smart_cities_spaces",{"_249":254,"_244":252},"industry","Object Detection","uscs_object_detection",{"_249":258,"_244":256},"application","NSPECT-UAGN-IT0B",{"_249":248,"_244":259},"NSPECT-FY5H-V6CO",{"_249":248,"_244":261},"English",{"_249":265,"_244":263},"language","AI","soln_ai",{"_249":269,"_244":267},"technology",{"_107":1443,"_109":1444},"ngcApps",[1396],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1377":1378,"_1379":1380,"_1381":1382,"_1383":1384,"_1385":281,"_138":305,"_1386":1387,"_1388":55,"_1389":86,"_1390":86,"_1391":1392,"_1393":86,"_1394":1395},"amplitude",{"_1374":1375,"_977":-7,"_1376":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1368":1369,"_1370":1371,"_1372":1373},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1252":215,"_1253":1254,"_1255":1256,"_1257":1258},"announcements",[],"consentState",{"_344":1242,"_1243":30,"_1244":30,"_1245":86,"_1246":1247},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1784021941201,[],[],[319,320,321,322,323,324],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":325,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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