Object detection, image classification, features extraction.
Use models trained in the cloud for your embedded applications!
Get high speed deep learning inference!
ailia is a deep learning middleware specialized in inference in the edge. Easily integrated in your application, it computes inference while making the best use of the GPU when available. We provide integral support to our product from the nice and consistent API to the optimized low layers, all developped in-house as fully proprietary solution.
Use models trained in the cloud for your embedded applications.
Using models trained on the cloud, you can implement easily your image recognition applications.
You don’t have to write anymore the pre- and post-processing, it is now provided by ailia in a utility class. Furthermore, you can use validated models publicly available on internet or provided by your business partners.
Unity plugin included.
A plugin for Unity is included. As webcam input is easily accessible inside Unity, you can take advantage of ailia’s C# API for your image recognition applications.
Supports weights compression.
Includes a proprietary weights compression method. As the weights are compressed before being sent to the edge side, there is a gain of up to 1/3 of transmission time and storage.
Multiplatform high-speed GPU inference.
Perform fast inference using the GPU from various platforms.
High speed inference made possible while not depending on some particular hardware maker.
Obstacles detection with YOLO
Using YOLO trained model, you can detect persons and cars positions.
You can also load your own weights for example learned through Darknet.
Estimation of face characteristics with Gender/Age/EmotionNet.
After processing by YOLO Face, check for gender, age or emotion using each of these networks.
Feature extraction with VGG16
With VGG16, you can extract features from your images.
Using their distance in features space, you can compute the resemblance between images,
and thus easily build a search-by-image engine.
Pose estimation with Acculus Pose
Corresponds to the pose estimation model provided by Acculus Inc.
Achieve fast pose estimation with an algorithm different from OpenPose.
We include a Python library to convert from Keras/Darknet to a format readable by ailia, letting you write easily your conversion scripts.
import keras2caffe keras2caffe.convert(model,"my_model.prototxt","my_model.caffemodel")
python darknet2caffe.py yolo.cfg yolo.weights yolo.prototxt yolo.caffemodel
The layers/networks below are currently supported. We can also provide help to add new ones.
|Activation||ReLU Sofmax||ReLU Sofmax|
|Concatenate , Merge||Concat||Concat|
AlexNet InceptionV3 XceptionV1 VGG16
SqueezeNet MobileNet LeNet Yolo ResNet
Interactive signage using Unity.
By capturing position and pose of the person in front of a digital signage, a 3D character can act correspondingly, creating an enjoyable interaction.
Person detection for a reception system.
Detecting an incoming visitor by image recognition, it is possible to display informations about his visit destination, etc.
People counter for a physical store customers analytics.
Based on image recognition, estimate the number, gender, and age of visting customers, and use these data for your marketing analysis.
Specifications and included items
|Input format||prototxt, caffemodel, ONNX (Comming Soon)|
|Supported OS||Windows, Mac, iOS, Android|
|Library format||Static, Dynamic|
|API||C++, C#, Python (Comming Soon)|
|GPGPU||MetalPerformanceShaders, RenderScript, C++AMP|
|SIMD instruction set||SSE2, AVX, NEON|
Thank you for your inquiry.
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Android is a trademark of Google Inc.
Mac is a trademark of Apple Inc.
Other listed products and services are trademarks of each company.