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EmotionNet Model Card

Model Overview

The model described in this card is a classification network, which aims to classify human emotion into 6 categories.

- Neutral\n- Happy\n- Surprise\n- Squint\n- Disgust\n- Scream\n

Model Architecture

This is a classification model with five fully connected layers.

Training Algorithm

This model was trained using the EmotionNet entrypoint in TAO. The training algorithm optimizes the network to minimize the categorical cross entropy loss for the emotion classes.

Training Data

EmotionNet v1.0 model was trained on MultiPie dataset with more than 750K images. The training dataset consists of images taken from cameras mounted at varied heights and angles. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. The facial landmarks labels were acquired from NVIDIA data factory team and applied to training. All data has been labeled including subjects with both profile and non-profile faces.

Multi-PIE dataset\nThe information to purchase and license Multi-PIE dataset can be found here

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth landmarks by human labellers. Please refer to the FPENet Model Card for the\ninstructions of landmarks labeling.

During the data collection, subjects are asked to perform posed emotion. The emotion labels are obtained during collection.

Performance

Evaluation Data

Dataset

The inference performance of EmotionNet model was measured against 71020 MultiPie images across a variety of subjects, illuminitation conditions, camera heights and camera angles.

Methodology and KPI

The KPI results for EmotionNet landmarks_v1 model are reported in the table below. Model is evaluated based on Precision, Recall, and f_scroe.

================  ===========  ========  =========  ============\ncontent             precision    recall    f_score    numsamples\n================  ===========  ========  =========  ============\ndisgust                0.8156   0.73016    0.77052          6300\nhappy                 0.81961   0.93027    0.87144         13480\nneutral               0.95134   0.94047    0.94587         33260\nscream                0.98347   0.99443    0.98892          7180\nsquint                0.76419   0.64815     0.7014          5400\nsurprise              0.93284   0.92593    0.92937          5400\nAverage               0.87784   0.86157    0.86792         71020\nWeighted_average      0.90191     0.902     0.9007         71020\n================  ===========  ========  =========  ============\n

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system 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

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

Device	Precision	Batch_size	FPS
Nano	FP16	1	1190
NX	FP16	1	4016
Xavier	FP16	1	5988
T4	FP16	1	19644

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 6.0 or TensorRT.

Primary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.

There are two flavors of the model:

trainable
deployable

The trainable model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train.\nThe deployable model is intended for efficient deployment on the edge using DeepStream or TensorRT.\nThe trainable and deployable models are encrypted and will only operate with the following key:

Model load key: nvidia_tlt

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

Input

68 points (X, Y) of Human facial landmarks (1 x 136 x 1)

The training pipeline can accept more input points, but the pre-train model is trained with 68 points input.

Output

Category labels (emotion) of each subject in the input image.

Instructions to use the model with TAO

In order to use this model as a pretrained weights for transfer learning, please use the below mentioned template for the model component of the experiment spec file to train a EmotionNet model.

model:\n  __class_name__: EmotionNetModel\n  model_parameters:\n    use_batch_norm: True\n    data_format: channels_first\n    regularization_type: l2\n    regularization_factor: 0.0015\n    bias_regularizer: null\n    use_landmarks_input: True\n    activation_type: 'relu'\n    dropout_rate: 0.3\n    num_class: 6\n

Instructions to deploy this model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with DeepStream.

Limitations

Spontaneous Emotion

NVIDIA EmotionNet model were trained with MultiPie datasets that have six emotion classes. The subjects in the datasets were performing pose emotion. Therefore it will not be able to detect emotion classes that are not belong to these classes or spontaneous emotions.

Unstable landmarks

NVIDIA EmotionNet model (landmarks_v1.0) does not give good results on detectioning emotion, if the landmarks are not stable (generally, this happens when the face are occluded).

Model versions

trainable_v1.0 - Pre-trained model that is intended for training.
deployabale_v1.0 - Deployment models that is intended to run on the inference pipeline.

References

Citations

Gross, R., Iain M., Jeffrey C., Takeo K., and Simon B.. \"Multi-pie.\" Image and Vision Computing 28, no. 5 (2010): 807-813.
Lucey, Patrick, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. \"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.\" In 2010 ieee computer society conference on computer vision and pattern recognition-workshops, pp. 94-101. IEEE, 2010.

Using TAO Pre-trained Models

Get TAO Container
Get other Purpose-built models from NGC model registry:\n
- FaceNet
- FPENet
- GazeNet
- EmotionNet
- GestureNet
- HeartRateNet
- PeopleNet
- TrafficCamNet
- DashCamNet
- VehicleMakeNet
- VehicleTypeNet
\n

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit

License

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

Ethical Considerations

NVIDIA EmotionNet model detects emotion categories. However, no additional information such as race, gender, and skin type about the faces is inferred.

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 developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction 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.

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The training algorithm optimizes the network to minimize the categorical cross entropy loss for the emotion classes.\n\n### Training Data \n\nEmotionNet v1.0 model was trained on MultiPie dataset with more than 750K images. The training dataset consists of images taken from cameras mounted at varied heights and angles. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. The facial landmarks labels were acquired from NVIDIA data factory team and applied to training. All data has been labeled including subjects with both profile and non-profile faces.\n\n[Multi-PIE dataset](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html)\nThe information to purchase and license Multi-PIE dataset can be found [here](https://cmu.flintbox.com/#technologies/67027840-27d5-4570-86dd-ad4715ef3c09)\n\n#### Training Data Ground-truth Labeling Guidelines \n\nThe training dataset is created by labeling ground-truth landmarks by human labellers. Please refer to the FPENet Model Card for the\ninstructions of landmarks labeling.\n\nDuring the data collection, subjects are asked to perform posed emotion. The emotion labels are obtained during collection.\n\n## Performance\n\n### Evaluation Data \n\n#### Dataset \n\nThe inference performance of EmotionNet model was measured against 71020 MultiPie images across a variety of subjects, illuminitation conditions, camera heights and camera angles.\n\n#### Methodology and KPI \n\nThe KPI results for EmotionNet landmarks_v1 model are reported in the table below. Model is evaluated based on Precision, Recall, and f_scroe.\n\n```\n================ =========== ======== ========= ============\ncontent precision recall f_score numsamples\n================ =========== ======== ========= ============\ndisgust 0.8156 0.73016 0.77052 6300\nhappy 0.81961 0.93027 0.87144 13480\nneutral 0.95134 0.94047 0.94587 33260\nscream 0.98347 0.99443 0.98892 7180\nsquint 0.76419 0.64815 0.7014 5400\nsurprise 0.93284 0.92593 0.92937 5400\nAverage 0.87784 0.86157 0.86792 71020\nWeighted_average 0.90191 0.902 0.9007 71020\n================ =========== ======== ========= ============\n```\n\n### Real-time Inference Performance \n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n| Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | --- |\n| Nano | FP16 |1| 1190 |\n| NX | FP16 | 1 | 4016 |\n| Xavier | FP16 | 1 | 5988 |\n| T4 | FP16 | 1 | 19644 |\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 6.0](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nPrimary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.\n\nThere are two flavors of the model:\n\n- trainable\n- deployable\n\nThe `trainable` model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. 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.\nThe `deployable` model is intended for efficient deployment on the edge using DeepStream or TensorRT.\nThe `trainable` and `deployable` models are encrypted and will only operate with the following key:\n\n- Model load key: `nvidia_tlt`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n### Input\n\n[68 points](https://www.studytonight.com/post/dlib-68-points-face-landmark-detection-with-opencv-and-python) (X, Y) of Human facial landmarks (1 x 136 x 1)\n\nThe training pipeline can accept more input points, but the pre-train model is trained with 68 points input.\n\n### Output\n\nCategory labels (emotion) of each subject in the input image.\n\n### Instructions to use the model with TAO \n\nIn order to use this model as a pretrained weights for transfer learning, please use the below mentioned template for the `model` component of the experiment spec file to train a EmotionNet model.\n\n```py\nmodel:\n __class_name__: EmotionNetModel\n model_parameters:\n use_batch_norm: True\n data_format: channels_first\n regularization_type: l2\n regularization_factor: 0.0015\n bias_regularizer: null\n use_landmarks_input: True\n activation_type: 'relu'\n dropout_rate: 0.3\n num_class: 6\n```\n\n### Instructions to deploy this model with DeepStream \n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps).\n\n## Limitations \n\n**Spontaneous Emotion**\n\nNVIDIA EmotionNet model were trained with MultiPie datasets that have six emotion classes. The subjects in the datasets were performing pose emotion. Therefore it will not be able to detect emotion classes that are not belong to these classes or spontaneous emotions.\n\n**Unstable landmarks**\n\nNVIDIA EmotionNet model (landmarks_v1.0) does not give good results on detectioning emotion, if the landmarks are not stable (generally, this happens when the face are occluded).\n\n## Model versions\n\n- **trainable_v1.0** - Pre-trained model that is intended for training.\n- **deployabale_v1.0** - Deployment models that is intended to run on the inference pipeline.\n\n## References\n\n### Citations \n\n- Gross, R., Iain M., Jeffrey C., Takeo K., and Simon B.. \"Multi-pie.\" Image and Vision Computing 28, no. 5 (2010): 807-813.\n- Lucey, Patrick, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. \"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.\" In 2010 ieee computer society conference on computer vision and pattern recognition-workshops, pp. 94-101. IEEE, 2010.\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 NGC model registry:\n - [FaceNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [FPENet](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [GazeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [EmotionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [GestureNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [HeartRateNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n\n### Technical blogs \n\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- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\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_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## License \n\nLicense to use this model is covered by the Model EULA. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \n\nNVIDIA EmotionNet model detects emotion categories. However, no additional information such as race, gender, and skin type about the faces is inferred.\n\nNVIDIA’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. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657],[602,603,604,605,606],{"_405":497,"_211":497,"_169":170,"_378":628},{"_405":463,"_211":463,"_169":170,"_378":623},{"_405":616,"_211":616,"_169":170,"_378":617},{"_405":612,"_211":612,"_169":170,"_378":613},{"_405":607,"_211":607,"_169":170,"_378":608},"nvcrm.my.site.com",[609],{"_79":610,"_411":607,"_412":31,"_169":611},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[614],{"_79":615,"_411":612,"_412":31,"_169":446},"edge","github.com",[618,619],{"_79":622,"_411":616,"_412":31,"_169":621},{"_79":620,"_411":616,"_412":31,"_169":621},"logged_in","This domain is owned by Github. 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