What you'll get
Tensorflow 2.1 & Python 3.7
CAER-S Dataset: 70,000
Train on GPU’s
Test on Raspberry Pi
Kynisys is hosting an exciting challenge: Emotion Detection in the Wild. We believe this will help build innovative new technologies for the future.
Create a model that can correctly predict the emotion class for an image, within the limitations of edge devices. Here are some more details to take into consideration when building your models.
Build models ensuring the right balance between speed, accuracy and resource consumption
Build solutions using Tensor 2.1
Build solutions by coding in Jupyter notebooks environment on our platform and use free compute resources in the cloud and device farm
Target edge devices are Raspberry Pi 3b+
You decide how to use the supplied training subset, including the split for training and validation
Test dataset will be ﬁxed and will be used to compute the public leaderboards
Dataset for training and testing is supplied by Kynisys.
The competitors will get access to the CAER-S Dataset: Context-Aware Emotion Recognition. It’s a large-scale static emotion recognition benchmark that contains more than 70,000 annotated images. The benchmark consists of train, validation and test folders.
Kynisys is offering a total prize pot of $10,000 to be won.
The Kynisys Platform
The platform allows competitors to code/build their solutions using Jupyter notebooks.
Supported framework is TensorFlow 2.1 on python 3.7
Competitors have unlimited CPU/GPU resources, however, GPU use may be throttled in the case of many concurrent competitors
Jupyter notebooks are running on CPU resources (no quota limitation) by default, these can be used to code and debug the solution
Once complete, a notebook can be run in a captive flow that trains on a GPU cluster and evaluates the test both on the training system and the Raspberry Pi edge device
The conversion to the edge device uses TensorFlow Lite post-training quantisation with the representative “dataset”
Emotion recognition use cases
Exciting Emotion Recognition use cases are emerging across a set of industries.
Prevention of terrorism, smuggling, aggression, modern slavery.
Detecting the mood or possible intentions of people in any public area enables alerts of suspicious activity to be flagged to security staff in real time.
Medical diagnosis, help autistic people understand emotions.
Using emotion detection to interpret genuine pain and discomfort including location, speeding up diagnosis and filtering out bogus cases.
Prevent fatigue &
The immediate detection of negative driver emotions (anger, fatigue, stress, confusion, etc.) or unwanted distractions can give a very early warning of danger or potential issues.
Official entry open
Final submission deadline
Multiple solutions per competitor can be submitted via Kynisys leaderboard
Competitor chooses which solution/run they submit
Public leaderboard will display scores obtained by running solutions on the test subset from the dataset
Score will be based on top-1 accuracy of the model, however, submissions will only be possible if the constraints on inference time (100ms) have been met