TAO Pretrained Commercial Backbone for Deformable DETR
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.
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 places bounding boxes around the object. This model card contains pretrained weights that may be used as a starting point with the Deformable-DETR object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.
It is trained on the NVImageNet that is permitted for commercial uses. Following backbones are supported with Deformable-DETR networks.
- gc_vit_xxtiny / gc_vit_xtiny / gc_vit_tiny / gc_vit_small / gc_vit_base / gc_vit_large / gc_vit_large_384
- resnet50 - NVImageNet pre-trained ResNet-50 model for finetune.
- gcvit_xxtiny_nvimagenet - NVImageNet pre-trained GCViT-xxTiny model for finetune.
- gcvit_xtiny_nvimagenet - NVImageNet pre-trained GCViT-xTiny model for finetune.
- gcvit_tiny_nvimagenet - NVImageNet pre-trained GCViT-Tiny model for finetune.
- gcvit_small_nvimagenet - NVImageNet pre-trained GCViT-Small model for finetune.
- gcvit_base_nvimagenet - NVImageNet pre-trained GCViT-Base model for finetune.
Instructions to Use Pretrained Backbone Models with TAO
To use these models as pretrained backbone weights for transfer learning, use the snippet below as a template 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.
Get TAO Object Detection pre-trained models for YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet architectures from NGC model registry
Get TAO DetectNet_v2 Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
Get TAO EfficientDet Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
Get TAO Instance segmentation pre-trained models for MaskRCNN architecture from NGC
Get TAO Semantic segmentation pre-trained models for UNet architecture from NGC
Get Purpose-built models from NGC model registry:
The licenses to use this model is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses
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