TOOLS DEVELOPED FOR IMAGE RECOGNITION
The tools we developed allow our Data Scientists to concentrate on actual high added value tasks for algorithms conception and implementation. We create leverage on three tasks: ease image labelisation process, offer a complete documentation on image recognition libraries, automate algorithms training.
It is the ratio of time a Data Scientist spend on low added value tasks. Our tools bring it down to 20%.
Launch of our R&D
Our tools let me focus on the code which requires the highest technical knowledge and the more human intelligence.
Accurate data is the success-driving factor for image recognition projects. Monitoring the quality of data ensures the team that the algorithm is correctly learning. What Chani offers: - 100% of the data is available for AI algorithm training - Data Scientists can create their datasets x2 faster - Data labeling is automated thanks to Deep Learning algorithms (YOLOv3, MaskRCNN)
Ibad allows data Scientists and project leaders to understand how algorithms train thanks to a state of the art library of image recognition algorithms. More precisely, Ibad allows to: - Refine algorithms to better solve our clients problems - Cut by three the time dedicated to defining the architecture of code - Maintain the state of the art knowledge of the image recognition ecosystem thanks to tutorials and self-learning sessions
Sicara dedicates 20% of its ressources to R&D to maintain its teams on the cutting-edge of technology in image recognition.
ENSTA ParisTech, PhD ENSAM
Associated articles written by Sicara's Data Scientists
GAN with Keras: Application to Image Deblurring
A Generative Adversarial Networks tutorial applied to Image Deblurring with the Keras library.
Keras Tutorial: Content Based Image Retrieval Using a Denoising Autoencoder
How to find similar images thanks to Convolutional Denoising Autoencoder.
Set up TensorFlow with Docker + GPU in Minutes
Why Docker is the best platform to use Tensorflow with a GPU.