TAO Pretrained Classification classifies an image into one of the designated thousands of classes. This model is ready for commercial use.
Get TAO Object Detection pre-trained models for YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet architectures from the 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:
Architecture Type: Convolution Neural Network (CNN)
Network Architecture(s):
Input Type(s): Image
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: 4D
Other Properties Related to Input: 4D includes batch size as fourth dimension. Minimum Resolution: B X 3 X 224 X 224; Maximum Resolution: B X 3 X 518 X 518; No minimum bit depth, alpha, or gamma
Output Type(s): Label(s)
Output Format: Label: Text String
Other Properties Related to Output: Category Label(s): Designated Classes
Runtime Engine(s):
Supported Hardware Architecture(s):
Supported Operating System(s):
Link: https://github.com/openimages/dataset/blob/main/READMEV3.md
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
Roughly 400,000 images and 7,000 validation images across thousands of classes as defined by Google OpenImages Version Three (3) dataset. Most of the human verifications have been done with in-house annotators at Google. A smaller part has been done with crowd-sourced verification from Image Labeler: Crowdsource app, g.co/imagelabeler.
Link: https://github.com/openimages/dataset/blob/main/READMEV3.md
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
15,000 test images from Google OpenImages Version Three (3) dataset.
The image classification apps in TAO expect data in KITTI file format. TAO provides a simple command line interface to train a deep learning model for image classification.
The models in this model area are only compatible with TAO Toolkit. For more information about TAO please refer to the Getting Started Guide.
Install the NGC CLI from ngc.nvidia.com
Configure the NGC CLI using the following command
ngc config set
ngc registry model list nvidia/tao_pretrained_classification:*
ngc registry model download-version nvidia/tao_pretrained_classification:<template> --dest <path>
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
Configure the NGC command line interface using the command mentioned below and follow the prompts.
ngc config set
ngc registry resource download-version "nvidia/tao_cv_samples:v1.0.2"
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
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.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.