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TLT Pretrained Object Detection

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Description

Pretrained weights to facilitate transfer learning using Transfer Learning Toolkit.

Publisher

NVIDIA

Use Case

Object Detection

Framework

Transfer Learning Toolkit

Latest Version

mobilenet_v1

Modified

August 24, 2021

Size

26.22 MB

What is Transfer Learning Toolkit?

Transfer Learning Toolkit (TLT) is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TLT 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 TLT, DeepStream SDK and TensorRT. The models are suitable for object detection and classification.

Object Detection Using TLT

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 following object detection networks in Transfer Learning Toolkit (TLT) to facilitate transfer learning.

  • YOLOV3
  • FasterRCNN
  • SSD
  • DSSD
  • RetinaNet

It is trained on a subset of the Google OpenImages dataset. Following backbones are supported with these detection networks.

  • resnet10/resnet18/resnet34/resnet50/resnet101
  • vgg16/vgg19
  • googlenet
  • mobilenet_v1/mobilenet_v2
  • squeezenet
  • darknet19/darknet53
  • efficientnet_b0
  • cspdarknet19/cspdarknet53

Some combinations might not be supported. See the matrix below for all supported combinations.

LOGO

To see the full list of all the backbones, scroll over to the version history tab.

Note: These are unpruned models with just the feature extractor weights, and may not be used without re-training in an object detection application

Note: The ResNet101 model is currently only supported for FasterRCNN currently. Please make sure to turn set the all_projections field to False in the spec file when training a ResNet101 model. For more information about this parameter please refer to the TLT Getting Started Guide.

Note: The pre-trained weights in this model are only for the detection networks above and shouldn't be used for DetectNet_v2 based object detection models. For pre-trained weights with DetectNet_v2, click here

Running Object Detection Models Using TLT

The object detection apps in TLT expect data in KITTI file format. TLT provides a simple command line interface to train a deep learning model for object detection.

The models in this model area are only compatible with Transfer Learning Toolkit. For more information about the TLT container, please visit the TLT container page.

Before running the container, use docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.

  1. Install the NGC CLI from ngc.nvidia.com

  2. To view all the backbones that are supported by object detection architecture in TLT:

ngc registry model list nvidia/tlt_pretrained_object_detection:*
  1. Download the model:
ngc registry model download-version nvidia/tlt_pretrained_object_detection:<template> --dest <path>

Instructions to run the sample notebook

  1. Get the NGC API key from the SETUP tab on the left. Please store this key for future use. Detailed instructions can be found here

  2. Configure the NGC command line interface using the command mentioned below and follow the prompts.

ngc config set
  1. Download the sample notebooks from NGC using the command below
ngc registry resource download-version "nvidia/tlt_cv_samples:v1.0.2"
  1. Invoke the jupyter notebook using the following command
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
  1. Open an internet browser and type in the following URL to start running the notebooks when running on a local machine.
http://0.0.0.0:8888

If you wish to run view the notebook from a remote client, please modify the URL as follows:

http://a.b.c.d:8888

Where, the a.b.c.d is the IP address of the machine running the container.

Other TLT Pre-trained Models

License

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, please visit this link, or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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Ethical AI

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 developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction 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.