ngc registry model list nvidia/tao_pretrained_object_detection:*

","code--hmf52z","

ngc registry model download-version nvidia/tao_pretrained_object_detection:<template> --dest <path>

","code--ehohuu","

ngc config set

","code-m0cu0r","

ngc registry resource download-version \"nvidia/tao_cv_samples:v1.0.2\"

","code--louxl4","

jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root

","code-urbyfq","

http://0.0.0.0:8888

","code--o715o4","

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

","code--fiu4h7","ResNet101","code--svlymy","all_projections","code-149u6b","False","code-qf2ce8","a.b.c.d","nvidia/tao/pretrained_object_detection","nvidia/tao/models/pretrained_object_detection","pretrained_object_detection","Pretrained weights to facilitate transfer learning using TAO Toolkit.","## What is Train Adapt Optimize (TAO) Toolkit? \n\n[Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/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. This model is ready for commercial use.\n\nThe 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.\n\nBuild end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO, [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). The models are suitable for object detection and classification.\n\n### Object Detection Using TAO \n\nObject 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 Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.\n\n- **YOLOV3**\n- **YOLOV4**\n- **YOLOV4-Tiny**\n- **FasterRCNN**\n- **SSD**\n- **DSSD**\n- **RetinaNet**\n\nIt is trained on a subset of the Google OpenImages dataset. Following backbones are supported with these detection networks.\n\n- resnet10/resnet18/resnet34/resnet50/resnet101\n- vgg16/vgg19\n- googlenet\n- mobilenet_v1/mobilenet_v2\n- squeezenet\n- darknet19/darknet53\n- efficientnet_b0\n- cspdarknet19/cspdarknet53\n- cspdarknet-tiny\n\nSome combinations might not be supported. See the matrix below for all supported combinations.\n\n![LOGO](https://github.com/vpraveen-nv/model_card_images/raw/main/api/openimage_table.jpg)\n\nTo see the full list of all the backbones, scroll over to the version history tab.\n\n***Note:*** These are unpruned models with just the feature extractor weights, and may not be used without re-training in an object detection application\n\n***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 [TAO Getting Started Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n***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](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_detectnet_v2)\n\n### Running Object Detection Models Using TAO \n\nThe object detection apps in TAO expect data in KITTI file format. TAO provides a simple command line interface to train a deep learning model for object detection.\n\nThe models in this model area are only compatible with TAO Toolkit. For more information about the TAO container, please visit the TAO container page.\n\nBefore 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.\n\n1. Install the NGC CLI from ngc.nvidia.com\n\n2. To view all the backbones that are supported by object detection architecture in TAO:\n\n```sh\nngc registry model list nvidia/tao_pretrained_object_detection:*\n```\n\n3. Download the model:\n\n```sh\nngc registry model download-version nvidia/tao_pretrained_object_detection: