Deep learning techniques are all set to transform businesses, in ways never seen before. The use of sophisticated, multi-level deep neural networks is giving businesses inferences, insights and decision making prowess as advanced as human cognition. PathPartner, with its holistic approach to implementing, optimizing and integrating deep learning methodologies for various applications, is your trusted partner in your deep learning adoption journey.

Full stack service offerings

Data preparation
  • Data preparation for both model training and testing
  • Data with variation to cover all possibilities
  • Annotation tools
  • Manual and semi-automatic labeling
Model design, training
  • Complexity aware custom architectures including FF-CNNs, RNNs, LSTMs, CRFs, and HMMs
  • Selecting right frameworks
  • Model training on standard and custom data-sets
Platform optimizations
  • Porting and optimizations for server grade and embedded platforms
  • Deep compression for embedded platforms
  • GPU acceleration and model conversion tools
Maintenance
  • Incremental upgrades to models
  • Porting and optimizations on new or upgraded frameworks and platforms

Our offerings span a wide range of applications

Driver monitoring
ADAS solutions
Precision farming
Medical imaging
Augmented reality

Our expertise in action PathPartner’s Driver Monitoring solution

PathPartner’s driver monitoring solution, uses advanced deep learning methodologies to assess drivers’ alertness under diverse conditions. It uses a hybrid combination of advanced facial analysis algorithms across 28 facial landmarks to perform real time analysis of driver’s eyes, mouth, and head movements. With a thorough study on each of these problems complexity, deep understanding of deep learning models’ representation abilities, we developed algorithms that can run real time on embedded systems (Ex: our DMS can run real time on Qualcomm's SD820 and NVidia TX2)

Know More

Platforms. Frameworks. Tools. Check.

Platforms
  • Server grade plaforms
  • Embedded platforms such as NVIDIA TX2, Qualcomm 820A, 625, 635, NXP’s S32V234 and TI’s TDA2x/3x, Candence Vision P5,P6,
Frameworks
  • Caffe/Caffe2
  • Tensorflow
Tools
  • Computation backend – CUDA, OpenBLAS
  • Data visualization : Pandas, Tensorboard, Matplotlib
  • Stats workbench : Python

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