TAO Pretrained EfficientDet

Description:

EfficientDet recognizes the individual objects in an image. This model is ready for commercial use.

References:

Citations

• Tan, Mingxing, Ruoming Pang, and Quoc V. Le. "Efficientdet: Scalable and efficient object detection." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

Other TAO Pre-trained Models

• Get TAO Object Detection pre-trained models for YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet architectures from NGC model registry
• Get TAO EfficientDet Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
• Get TAO classification pre-trained models 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:
  • PeopleNet
  • TrafficCamNet
  • DashCamNet
  • FaceDetectIR
  • VehicleMakeNet
  • VehicleTypeNet
  • PeopleSegNet
  • PeopleSemSegNet
  • License Plate Detection
  • License Plate Recognition
  • Facial Landmark
  • FaceDetect
  • 2D Body Pose Net
  • ActionRecognitionNet

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
Network Architecture: EfficientNet

The models in this instance are feature extractors based on the EfficientNet architecture.

Input:

Input Type(s): Image
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: 3D
Other Properties Related to Input: RGB Fixed Resolution: 224 X 224 X 3 (W x H x C); No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Label(s), Bounding-Box(es), Confidence Scores
Output Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
Other Properties Related to Output: Category Label(s): (Labels of object detected), Bounding Box Coordinates, Confidence Scores

Software Integration:

Runtime Engine(s):

• DeepStream 6.1 or later
  
• TAO - 5.2
  

Supported Hardware Architecture(s):

• Ampere
  
• Jetson
  
• Hopper
  
• Lovelace
  
• Pascal
  
• Turing
  

Supported Operating System(s):

• Linux
  
• Linux 4 Tegra
  

Model Version(s):

The following efficientnet-x backbone versions are supported in TAO Toolkit:

• efficientnet-b0
• efficientnet-b1
• efficientnet-b2
• efficientnet-b3
• efficientnet-b4
• efficientnet-b5

Training & Evaluation:

Training Dataset:

Link: https://github.com/openimages/dataset/blob/main/READMEV3.md
Data Collection Method by dataset:

• Unknown
  

Labeling Method by dataset:

• Unknown
  

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.

Evaluation Dataset:

Link: https://github.com/openimages/dataset/blob/main/READMEV3.md

Data Collection Method by dataset:

• Unknown
  

Labeling Method by dataset:

• Unknown
  

Properties:

• 15,000 test images from Google OpenImages Version Three (3) dataset.

Inference:

Engine: Tensor(RT)
Test Hardware:

• Jetson AGX Xavier
• Xavier NX
• Orin
• Orin NX
• NVIDIA T4
• Ampere GPU
• A2
• A30
• L4
• T4
• DGX H100
• DGX A100
• DGX H100
• L40
• JAO 64GB
• Orin NX16GB
• Orin Nano 8GB

How to Use this Model

Running EfficientDet Models Using TAO

The EfficientDet app in TAO expect data in COCO format. TAO 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 TAO Toolkit. For more information about the TAO container, please visit the TAO container page.

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

2. Configure the NGC CLI using the following command

ngc config set

3. To view all the backbones that are supported by object detection architecture in TAO:

ngc registry model list nvidia/tao/pretrained_efficientdet:*

4. To download the model:

ngc registry model download-version nvidia/tao/pretrained_efficientdet:<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

3. Download the sample notebooks from NGC using the command below

ngc registry resource download-version "nvidia/tao/cv_samples:v1.3.0"

4. Invoke the jupyter notebook using the following command

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

5. 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.

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• Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
• Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO

Suggested reading

• More information about TAO Toolkit and pre-trained models can be found at the NVIDIA Developer Zone
• TAO documentation
• Read the TAO getting Started. guide and release notes.
• If you have any questions or feedback, please refer to the discussions on TAO Toolkit Developer Forums.
• Deploy your models for video analytics application using DeepStream. Learn more about DeepStream SDK.
• Deploy your models in Riva for ConvAI use case.

Ethical Considerations:

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.