The model described in this card is a classification network, which aims to classify human emotion into 6 categories.
- Neutral - Happy - Surprise - Squint - Disgust - Scream
This is a classification model with five fully connected layers.
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.
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.
The training dataset is created by labeling ground-truth landmarks by human labellers. Please refer to the FPENet Model Card for the instructions of landmarks labeling.
During the data collection, subjects are asked to perform posed emotion. The emotion labels are obtained during collection.
The inference performance of EmotionNet model was measured against 71020 MultiPie images across a variety of subjects, illuminitation conditions, camera heights and camera angles.
The KPI results for EmotionNet landmarks_v1 model are reported in the table below. Model is evaluated based on Precision, Recall, and f_scroe.
================ =========== ======== ========= ============ content precision recall f_score numsamples ================ =========== ======== ========= ============ disgust 0.8156 0.73016 0.77052 6300 happy 0.81961 0.93027 0.87144 13480 neutral 0.95134 0.94047 0.94587 33260 scream 0.98347 0.99443 0.98892 7180 squint 0.76419 0.64815 0.7014 5400 surprise 0.93284 0.92593 0.92937 5400 Average 0.87784 0.86157 0.86792 71020 Weighted_average 0.90191 0.902 0.9007 71020 ================ =========== ======== ========= ============
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.
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 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.
deployable model is intended for efficient deployment on the edge using DeepStream or TensorRT.
deployable models are encrypted and will only operate with the following key:
Please make sure to use this as the key for all TAO commands that require a model load key.
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.
Category labels (emotion) of each subject in the input image.
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: __class_name__: EmotionNetModel model_parameters: use_batch_norm: True data_format: channels_first regularization_type: l2 regularization_factor: 0.0015 bias_regularizer: null use_landmarks_input: True activation_type: 'relu' dropout_rate: 0.3 num_class: 6
To create the entire end-to-end video analytic application, deploy this model with DeepStream.
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.
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).
License to use this model is covered by the Model EULA. By downloading the model, you accept the terms and conditions of these licenses.
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.