NVIDIA Transfer Learning Toolkit
Eliminate the time-consuming process of building and fine-tuning Deep Neural Networks (DNNs) from scratch for Intelligent Video Analytics (IVA) applications.
The term “transfer learning” implies that you can extract learned features from an existing neural network and transfer these learned features by transferring weight from an existing neural network. The pre-trained models accelerate the developer’s deep learning training process and eliminate higher costs associated with large scale data collection, labeling, and training models from scratch. Transfer Learning Toolkit enables you to build high performance IVA based applications such as retail analytics, logistics, smart cities, access control and more.
The Transfer Learning Toolkit is a python-based toolkit that enables developers to take advantage of NVIDIA’s pre-trained models and offers capabilities for developers to adapt popular network architectures and backbones to their own data, train, fine tune, prune and export for deployment. The simple interface and abstraction improves the efficiency of the deep learning training workflow.
- GPU optimized pre trained weights for computer vision tasks
- Easily modify configuration files for adding new classes and retraining models with custom data
- Perform model adaptation and retraining in heterogeneous multi- GPU environments
- Reduce model sizes using pruning functionality
- Model Export API for deployment on NVIDIA DeepStream SDK with NVIDIA Tesla and Jetson products
- Jupyter notebook examples for object classification and detection use cases
Pre-trained IVA specific image classification and object detection models trained on selected public datasets are available to be used with Transfer Learning Toolkit.
- MobileNet V1/V2
- MobileNet V1/V2
Faster RCNN supporting backbones:
- VGG 16/19
- MobileNet V1/V2
DetectNet_v2 supporting backbones:
Enabling End to End Deep Learning Workflow for Intelligent Video Analytics
For developers designing and integrating IVA end applications such as parking management, securing critical infrastructure, retail analytics, logistics management and access control, etc. NVIDIA provides end to end deep learning workflow with DeepStream SDK for AI-based video and image understanding, as well as multi-sensor processing. DeepStream and Transfer Learning Toolkit together are an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for transforming pixels and sensor data to actionable insights.
- Transfer Learning Toolkit is free and can be downloaded from NVIDIA NGC
- Pre trained models can be downloaded for free to be used with the software
- If you would like to use tlt-converter to run the downloaded models with NVIDIA DeepStream SDK and to convert model from UFF to a TensorRT engine. Download tlt-converter
Get Started With Hands-On Training
The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, engineers and researchers in AI and accelerated computing. Sign up for a full day workshop focused on deep learning for IVA by contacting DLI directly.
Tutorials & Technical Blogs
- Build and Deploy Accurate Deep Learning Models for Intelligent Image and Video Analytics
- Pruning models with NVIDIA Transfer Learning Toolkit
- What is transfer learning?
- Accelerating Intelligent Video Analytics using Transfer Learning Toolkit
- Breaking the Boundaries of Intelligent Video Analytics with DeepStream SDK
- Real-time object detection for disaster response
- DeepStream SDK- Accelerating Real-Time AI Based Video And Image Analytics
- An SDK to Improve Video Analytics
- Developer guide
- Developer news article: A Toolkit to fine-tune deep neural networks and simplify training tasks for Intelligent Video Analytics
- Post your questions or feedback in the Transfer Learning Toolkit forum