Train Adapt Optimize (TAO) Toolkit is a Python-based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO 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.
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 TAO DeepStream SDK and TensorRT. These models are suitable for object detection, classification, and segmentation.
Mask Auto-Labeler (MAL) is a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. This model card contains pre-trained weights for MAL trained on the COCO dataset to facilitate transfer learning through TAO Toolkit.
The model in this instance is an instance segmentator that takes color (RGB) images and bounding boxes as inputs and generates segmentation masks as outputs. The backbone feature extractor of this model is ViT-MAE-Base that were pre-trained on the ImageNet dataset.
This model was trained using the MAL entrypoint in TAO. The training algorithm optimizes the network to minimize the Multiple Instance Learning (MIL) loss and the Conditional Random Field (CRF) loss for the objects.
MAL was trained on the COCO 2017 dataset. The COCO dataset contains 118K training images and 5K validation images and corresponding annotation files. The annotation contains bounding boxes for 80 object categories.
We test the MAL model on the COCO 2017 validation dataset.
The key performance indicator is the mean Intersection over Union (mIoU) following the standard evaluation protocol for segmentation. The KPI for the evaluation data are reported below.
These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU. These models can only be used with TAO Toolkit.
The primary use case for these models is weakly supervised instance segmentation and auto-labeling.
It is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of object detection. High fidelity models can be trained to new use cases. A Jupyter notebook is available as a part of TAO container and can be used to re-train.
Segmentation mask for each detected objects in the input image.
To use these models as pretrained weights for transfer learning, use the snippet below as atemplate for the
train component of the experiment spec file to train a Deformable DETR model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.
checkpoint: /path/to/vit-mae-base-16epoch=10.pth model: arch: 'vit-mae-base/16'
MAL was trained on the COCO dataset with 80 object categories. Hence, the model may not perform well on data from completely different domains. We recommend further fine tuning on the target domain to get higher mIoU.
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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.