ActionRecognitionNet Model Card

Description:

ActionRecognitionNet recognizes actions of people in a sequence of video frames:

walk,
bike riding,
running,
Falling on the floor, and
push

This model is ready for commercial use.

References:

Citations

H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A Large Video Database for Human Motion Recognition. In: ICCV(2011)
Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199. 2014 Jun 9.
Carreira J, Zisserman A. Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017 (pp. 6299-6308).
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)

Using TAO Pre-trained Models

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

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: ResNet18

Input:

Input Type(s): Images from video
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: (3D, 4D)
\nOther Properties Related to Input:

RGB model:\n
- 3D model: 3 X 32 X 224 X 224 (C x D x H x W)
- 2D model: 96 X 224 X 224 (C x D H W)
\n
Optical flow model:\n
- 3D model: 2 X 32 x 224 x 224 (C x D x H x W)
- 2D model: 64 X 224 X 224 (CxD x H x W)
\n

Output:

Output Type(s): Label(s)
\nOutput Format: Label: Text String
\nOther Properties Related to Output: Category Label(s):walk, ride_bike, run, fall_floor, and push

Software Integration:

Runtime Engine(s):

TAO - 5.2
DeepStream 6.1 or later

Supported Hardware Architecture(s):

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing
Volta

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

trainable_v1.0 - Pre-trained models for 2D/3D RGB-Only and OF-Only models.
deployable_v1.0 - Models for 2D/3D RGB-Only models deployable to deepstream.

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Unknown

Labeling Method by dataset:

Unknown

Properties:
\nTrained on 1024 Videos of people walking, bike-riding,, running, falling on the floor, and pushing. Videos are varied in visible body parts, camera motion, camera viewpoint, number of people involved in the action and video quality.

\n
Class distribution:
\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\">
classes number of videos
walk 494
ride_bike 93
run 209
fall_floor 123
push 105
\n
\n
visible body parts: upper body, full body, lower body
\n
\n
camera motion: motion, static
\n
\n
camera view point: front, back, left, right
\n
\n
number of people involved in the action: single, two, three
\n
\n
video quality: good, medium, bad
\n
\n
video size: most of videos are in 320x240
\n

classes	number of videos
walk	494
ride_bike	93
run	209
fall_floor	123
push	105

Data Format

The data format must be in the following format.

/Dataset_01\n    /class_1\n        /video_1\n            /rgb\n                0000.png\n                0001.png\n                0002.png\n                ...\n                ...\n                ...\n                N.png\n            /u\n                0000.jpg\n                0001.jpg\n                0002.jpg\n                ...\n                ...\n                ...\n                N.jpg\n            /v\n                0000.jpg\n                0001.jpg\n                0002.jpg\n                ...\n                ...\n                ...\n                N.jpg\n

TAO toolkit support training ActionRecognitionNet with RGB input or optical flow input. The dataset should be divided into different directory by classes. Each of classes directory will contain multiple video clips folders which contain the corresponding RGB frames (rgb), optical flow x-axis grayscale images (u), and optical flow y-axis grayscale images (v).

Evaluation Dataset:

Data Collection Method by dataset:

Unknown

Labeling Method by dataset:

Unknown

Properties:
\nThe evaluation dataset are obtained by randomly collecting 10% video per class out of HMDB5. The videos are also diversed by visible body parts/camera motion/camera viewpoint/number of people involved in the action/video quality.

Methodology and KPI

The key performance indicator is the accuracy of action recognition. The center evaluation inference is performed on the middle part of frames in the video clip. For example, if the model requires 32 frames as input and a video clip has 128 frames, then we will choose the frames from index 48 to index 79 to do the inference. The conv evaluation inference is performed on 10 segments out of a video clip. We uniformly divide the video clip into 10 parts, choose center of each segments as start point and then pick 32 consecutive frames from those start points to form the inference segments. And the final label of the video is determined by the average score of those 10 segments.

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

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

model	dataset	center accuracy	conv accuracy
resnet18_2d_rgb_hmdb5_32	HMDB5	84.69%	82.88%
resnet18_3d_rgb_hmdb5_32	HMDB5	84.69%	85.59%
resnet18_2d_of_hmdb5_32_a100	HMDB5	78.38%	81.08%
resnet18_2d_of_hmdb5_32_xavier	HMDB5	80.18%	82.88%
resnet18_3d_of_hmdb5_32_a100	HMDB5	91.89%	92.79%
resnet18_3d_of_hmdb5_32_xavier	HMDB5	90.99%	95.50%

Inference:

Engine: Tensor(RT)
\nTest Hardware:

Jetson AGX Xavier
Xavier NX
Orin
Orin NX
NVIDIA T4
Ampere GPU
A2
A30
L4
T4
DGX H100
DGX A100
DGX H100
L40
JAO 64GB
Orin NX16GB
Orin Nano 8GB

The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, Xavier NX, AGX Xavier and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

\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\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 Type	Device	precision	batch_size	FPS
3D-RGB	Jetson Nano	FP16	1	0.57
3D-RGB	Jetson NX	FP16	4	4.9
3D-RGB	Jetson Xavier	FP16	4	33
3D-RGB	T4	FP16	4	137
2D-RGB	Jetson Nano	FP16	1	30
2D-RGB	Jetson NX	FP16	16	250
2D-RGB	Jetson Xavier	FP16	16	490
2D-RGB	T4	FP16	16	1818

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.

Primary use case intended for this model is to recognize the action from the sequence of RGB frames and optical flow gray images. The sequence number is 32.

There are six models provided:

resnet18_2d_rgb_hmdb5_32
resnet18_3d_rgb_hmdb5_32
resnet18_2d_of_hmdb5_32_a100
resnet18_2d_of_hmdb5_32_xavier
resnet18_3d_of_hmdb5_32_a100
resnet18_3d_of_hmdb5_32_xavier

They are intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of action recognition. High fidelity models can be trained to the new use cases. The Jupyter notebook available as a part of TAO container can be used to re-train.

These models are also intended for easy deployment to the edge using DeepStream SDK or TensorRT. DeepStream provides facility to create efficient video analytic pipelines to capture, decode and pre-process the data before running inference.

The models are encrypted and can be decrypted with the following key:

Model load key: nvidia_tao

Please make sure to use this as the key for all TAO commands that require a model load key.

Instructions to use the model with TAO toolkit

In order to use these models as pretrained weights for transfer learning, please use the snippet below as a template for the model_config component of the experiment spec file to train a 2D/3D ActionRecognitionNet. For more information on experiment spec file, please refer to the Train Adapt Optimize (TAO) Toolkit User Guide.

model_config:                                                                       \n  model_type: rgb\n  # model_type: of                                                                   \n  input_type: \"2d\"\n  # input_type: \"3d\"                                                                   \n  backbone: resnet18                                                                \n  rgb_seq_length: 32                                                                \n  rgb_pretrained_model_path: /workspace/action_recognition/resnet18_2d_rgb_hmdb5_32.tlt\n  # rgb_pretrained_model_path: /workspace/action_recognition/resnet18_3d_rgb_hmdb5_32.tlt\n  rgb_pretrained_num_classes: 5\n  # of_pretrained_model_path: /workspace/action_recognition/resnet18_2d_of_hmdb5_32_a100.tlt\n  # of_pretrained_num_classes: 5\n  sample_rate: 1\n

Instructions to deploy the model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with 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 6.0, please refer to the sample code: sources/apps/sample_apps/deepstream-3d-action-recognition/ in Deepstream SDK

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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 supporting 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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.

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Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A Large Video Database for Human Motion Recognition. In: ICCV(2011)\n- Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199. 2014 Jun 9.\n- Carreira J, Zisserman A. Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017 (pp. 6299-6308).\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n### Using TAO Pre-trained Models \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-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 - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\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 - [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## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** ResNet18
\n\n## Input:\n**Input Type(s):** Images from video
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** (3D, 4D)
\n**Other Properties Related to Input:**
\n- RGB model: \n - 3D model: 3 X 32 X 224 X 224 (C x D x H x W)\n - 2D model: 96 X 224 X 224 (C x D H W)\n- Optical flow model:\n - 3D model: 2 X 32 x 224 x 224 (C x D x H x W)\n - 2D model: 64 X 224 X 224 (CxD x H x W)\n\n\n## Output:\n**Output Type(s):** Label(s)
\n**Output Format:** Label: Text String
\n**Other Properties Related to Output:** Category Label(s):walk, ride_bike, run, fall_floor, and push
\n\n## Software Integration:\n**Runtime Engine(s):** \n* TAO - 5.2
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture(s):**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n* Volta
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \n- **trainable_v1.0** - Pre-trained models for 2D/3D RGB-Only and OF-Only models.\n- **deployable_v1.0** - Models for 2D/3D RGB-Only models deployable to deepstream.\n\n## Training & Evaluation: \n\n## Training Dataset:\n**Data Collection Method by dataset:**
\n* Unknown
\n\n**Labeling Method by dataset:**
\n* Unknown
\n\n**Properties:**
\nTrained on 1024 Videos of people walking, bike-riding,, running, falling on the floor, and pushing. Videos are varied in visible body parts, camera motion, camera viewpoint, number of people involved in the action and video quality. \n\n- Class distribution:\n\n |classes|number of videos|\n |:---:|:---:|\n |walk|494|\n |ride_bike|93|\n |run|209|\n |fall_floor|123|\n |push|105|\n\n- visible body parts: upper body, full body, lower body\n- camera motion: motion, static\n- camera view point: front, back, left, right\n- number of people involved in the action: single, two, three\n- video quality: good, medium, bad\n- video size: most of videos are in 320x240\n\n### Data Format\n\nThe data format must be in the following format.\n```py\n/Dataset_01\n /class_1\n /video_1\n /rgb\n 0000.png\n 0001.png\n 0002.png\n ...\n ...\n ...\n N.png\n /u\n 0000.jpg\n 0001.jpg\n 0002.jpg\n ...\n ...\n ...\n N.jpg\n /v\n 0000.jpg\n 0001.jpg\n 0002.jpg\n ...\n ...\n ...\n N.jpg\n```\nTAO toolkit support training ActionRecognitionNet with RGB input or optical flow input. The dataset should be divided into different directory by classes. Each of classes directory will contain multiple video clips folders which contain the corresponding RGB frames (rgb), optical flow x-axis grayscale images (u), and optical flow y-axis grayscale images (v).\n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Unknown
\n\n**Labeling Method by dataset:**
\n* Unknown
\n\n**Properties:**
\nThe evaluation dataset are obtained by randomly collecting 10% video per class out of HMDB5. The videos are also diversed by visible body parts/camera motion/camera viewpoint/number of people involved in the action/video quality.\n\n### Methodology and KPI\n\nThe key performance indicator is the accuracy of action recognition. The `center` evaluation inference is performed on the middle part of frames in the video clip. For example, if the model requires 32 frames as input and a video clip has 128 frames, then we will choose the frames from index 48 to index 79 to do the inference. The `conv` evaluation inference is performed on 10 segments out of a video clip. We uniformly divide the video clip into 10 parts, choose center of each segments as start point and then pick 32 consecutive frames from those start points to form the inference segments. And the final label of the video is determined by the average score of those 10 segments.\n\n|model|dataset|center accuracy|conv accuracy|\n|---|---|---|---|\n|resnet18_2d_rgb_hmdb5_32|HMDB5|84.69%|82.88%|\n|resnet18_3d_rgb_hmdb5_32|HMDB5|84.69%|85.59%|\n|resnet18_2d_of_hmdb5_32_a100|HMDB5|78.38%|81.08%|\n|resnet18_2d_of_hmdb5_32_xavier|HMDB5|80.18%|82.88%|\n|resnet18_3d_of_hmdb5_32_a100|HMDB5|91.89%|92.79%|\n|resnet18_3d_of_hmdb5_32_xavier|HMDB5|90.99%|95.50%|\n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- Jetson AGX Xavier\n- Xavier NX\n- Orin\n- Orin NX\n- NVIDIA T4\n- Ampere GPU\n- A2\n- A30\n- L4\n- T4\n- DGX H100\n- DGX A100\n- DGX H100\n- L40\n- JAO 64GB\n- Orin NX16GB\n- Orin Nano 8GB\n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson Nano, Xavier NX, AGX Xavier and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n|Model Type|Device|precision|batch_size|FPS|\n|---|---|---|---|---|\n|3D-RGB|Jetson Nano|FP16|1|0.57|\n|3D-RGB|Jetson NX|FP16|4|4.9|\n|3D-RGB|Jetson Xavier|FP16|4|33|\n|3D-RGB|T4|FP16|4|137|\n|2D-RGB|Jetson Nano|FP16|1|30|\n|2D-RGB|Jetson NX|FP16|16|250|\n|2D-RGB|Jetson Xavier|FP16|16|490|\n|2D-RGB|T4|FP16|16|1818|\n\n\n## How to use this model \n\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nPrimary use case intended for this model is to recognize the action from the sequence of RGB frames and optical flow gray images. The sequence number is 32.\n\nThere are six models provided:\n- resnet18_2d_rgb_hmdb5_32\n- resnet18_3d_rgb_hmdb5_32\n- resnet18_2d_of_hmdb5_32_a100\n- resnet18_2d_of_hmdb5_32_xavier\n- resnet18_3d_of_hmdb5_32_a100\n- resnet18_3d_of_hmdb5_32_xavier\n\nThey are intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of action recognition. High fidelity models can be trained to the new use cases. The Jupyter notebook available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\n\nThese models are also intended for easy deployment to the edge using DeepStream SDK or TensorRT. DeepStream provides facility to create efficient video analytic pipelines to capture, decode and pre-process the data before running inference.\n\nThe models are encrypted and can be decrypted with the following key:\n\n- Model load key: `nvidia_tao`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n### Instructions to use the model with TAO toolkit\n\nIn order to use these models as pretrained weights for transfer learning, please use the snippet below as a template for the `model_config` component of the experiment spec file to train a 2D/3D ActionRecognitionNet. For more information on experiment spec file, please refer to the [Train Adapt Optimize (TAO) Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\nmodel_config: \n model_type: rgb\n # model_type: of \n input_type: \"2d\"\n # input_type: \"3d\" \n backbone: resnet18 \n rgb_seq_length: 32 \n rgb_pretrained_model_path: /workspace/action_recognition/resnet18_2d_rgb_hmdb5_32.tlt\n # rgb_pretrained_model_path: /workspace/action_recognition/resnet18_3d_rgb_hmdb5_32.tlt\n rgb_pretrained_num_classes: 5\n # of_pretrained_model_path: /workspace/action_recognition/resnet18_2d_of_hmdb5_32_a100.tlt\n # of_pretrained_num_classes: 5\n sample_rate: 1\n```\n\n\n### Instructions to deploy the model with DeepStream\n\nTo 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.\n\nTo deploy this model with [DeepStream 6.0](https://developer.nvidia.com/deepstream-sdk), please refer to the sample code: sources/apps/sample_apps/deepstream-3d-action-recognition/ in Deepstream SDK\n\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## 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- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) 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 models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## Ethical Considerations:\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 supporting 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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.","displayName","Action Recognition Net",["D",1744225766325],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/actionrecognitionnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",332020614,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Recognizing actions of people. |\n| Model Type: | Action Recognition |\n| Intended Users: | An Action Recognition model is intended to benefit developers, researchers, and organizations working on video analysis and surveillance, healthcare providers monitoring patient activities, sports analysts studying player movements, and industries requiring automated systems to understand and respond to human actions, such as autonomous vehicles and interactive robots. |\n| Output: | Action Labels |\n| Describe how the model works: | Predicts action of person in an image |\n| Technical Limitations: | NVIDIA ActionRecognitionNet is trained on HMDB5 which is a subset of HMDB51 containing 1024 videos in total. So it is expected the accuracy of the model on videos other than those from HMDB5 is not at the same level as the number reported in performance section. Model needs to be transferred learned before deployment. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Top-1 : Precision |\n| Potential Known Risks: | This model may fail to detect actions. |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | Unknown |\n| Was consent obtained for any PII used? | Not applicable |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | No, all data was collected by Brown University Research Group |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Not Known (Collected by Third-Party) |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Activity Recognition (Video) |\n| Describe the life-critical application (if present). | None: Not within Operational Design Domain |\n| Use Case Restrictions: | Abide by https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/ | \n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[282,283,284,285,286],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_245":301,"_288":302,"_289":303,"_291":292},{"_245":299,"_288":299,"_289":300,"_291":292},{"_245":297,"_288":297,"_289":298,"_291":292},{"_245":295,"_288":295,"_289":296,"_291":292},{"_245":287,"_288":287,"_289":290,"_291":292},"TAO Toolkit","value","filter",{"_293":294,"_288":287},"category","label","field","framework","TAO",{"_293":292,"_288":295},"Deep Learning",{"_293":292,"_288":297},"CV",{"_293":292,"_288":299},"Computer Vision","soln_computer_vision",{"_293":304,"_288":302},"technology",{"_112":1283,"_114":1284},"ngcApps",[1236],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1217":1218,"_1219":1220,"_1221":1222,"_1223":1224,"_1225":316,"_144":340,"_1226":1227,"_1228":56,"_1229":91,"_1230":91,"_1231":1232,"_1233":91,"_1234":1235},"amplitude",{"_1214":1215,"_853":-7,"_1216":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1208":1209,"_1210":1211,"_1212":1213},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1093":172,"_1094":1095,"_1096":1097,"_1098":136},"announcements",[],"consentState",{"_378":1083,"_1084":91,"_1085":31,"_1086":31,"_1087":1088},"consentConfig",{"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379},"showContactAdmin",1784638250236,[],[],[354,355,356,357,358,359],{"_45":59,"_47":70,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":107,"_51":-5,"_53":-5,"_55":56},"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 tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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Name	Size	Updated	Actions
experiment.yaml	987 B	March 1, 2025 UTC	curl wget Download
resnet18_3d_of_hmdb5_32_a100.tlt	316.49 MB	March 1, 2025 UTC	curl wget Download