TAO Pretrained DINO

What is Train Adapt Optimize (TAO) Toolkit?

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.

Model Overview

Object detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and place bounding boxes around the object. This model card contains pre-trained weights for the DINO object detection networks trained on COCO dataset to facilitate transfer learning through the Train Adapt Optimize (TAO) Toolkit.

Model Architecture

The models in this instance are object detectors that take RGB images as input and produce bounding boxes and classes as output. There are four different types of feature extractors for DINO: ResNet50, GCViT-Tiny, FAN-Small, and FAN-Large. The backbone networks are classification networks that were pre-trained on the ImageNet1K dataset--except for FAN-Large, which was pre-trained on the ImageNet22K dataset with an input resolution of 384 x 384 for higher accuracy.

Training

This model was trained using the DINO entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects.

Training Data

DINO was trained on the COCO 2017 dataset, which contains 118K training images and 5K validation images with corresponding annotation files. The annotation contains bounding boxes for 80 object categories.

Performance

Evaluation Data

We have tested the DINO model on the COCO 2017 validation dataset.

Methodology and KPI

The key performance indicator is the mean average precision (mAP), following the standard evaluation protocol for object detection. The KPI for the evaluation data are reported below.

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