Train Adapt Optimize (TAO) Toolkit is a Python-based AI toolkit for customizing purpose-built pre-trained AI models with your own data. TAO Toolkit adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.
The pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.
Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using the TAO DeepStream SDK and TensorRT. These models are suitable for object detection, classification, and segmentation.
The model described in this card can be used as a pre-trained starting weights for SegFormer semantic segmentation task. The weights were trained with Image classification pipe on Internal Imagenet dataset.
FAN (Fully Attentional Network) is a transformer-based family of backbone from NVIDIA research that achieves SOTA in robustness against various corruptions. This family of backbone can easily generalize to new domains, be more robust to noise, blur etc. Key design behind FAN block is the attentional channel processing module that leads to robust representation learning. FAN can be used for image classification tasks as well as downstream tasks such as object detection and segmentation. Use FAN when your testing dataset has a domain gap from the training dataset.
Segformer is a real-time state of the art transformer based semantic segmentation model. SegFormer is a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. It then predicts a class label for every pixel in the input image.
This model was trained using the
classification_pyt entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss.
We have tested the FAN models on the ImageNet1K validation dataset.
The key performance indicator is the accuracy, following the standard evaluation protocol for image classification. The KPI for the evaluation data are reported below.
The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using
trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.
FAN-B-H-384 (384 resolution)
|Jetson Orin Nano||4||16|
|Orin NX 16GB||4||23.4|
|AGX Orin 64GB||8||61.2|
|Jetson Orin Nano||-||-|
|Orin NX 16GB||-||-|
|AGX Orin 64GB||-||-|
These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.
The primary use case for these models is classifying objects in a color (RGB) image. The model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing.
These models are intended for training and fine-tune using TAO Toolkit and user datasets for image classification. High-fidelity models can be trained to the new use cases. A Jupyter notebook is available as a part of the TAO container and can be used to re-train.
The models are also intended for easy edge deployment using DeepStream SDK or TensorRT. DeepStream provides the facilities to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference.
Category labels (1000 classes) for the input image.
To use these models as pretrained weights for transfer learning, please use the snippet below as template for the
train component of the experiment spec file to train a FAN Classification model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.
model: init_cfg: checkpoint: /path/to/the/fan_hybrid_base.pth backbone: type: fan_base_16_p4_hybrid custom_args: use_rel_pos_bias: True head: type: LinearClsHead
Documentation to deploy with DeepStream is provided in "Deploying to DeepStream" chapter of TAO User Guide.
FAN was trained on the ImageNet1K dataset with 1000 object categories. Hence the model may not perform well on different data distributions, so we recommend conducting further finetuning on the target domain to get higher accuracy.
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NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.