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:\n
- PeopleNet
- TrafficCamNet
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Facial Landmark
- FaceDetect
- 2D Body Pose Net
- ActionRecognitionNet
\n

Model Architecture:

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

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

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 3D
\nOther 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
\nOutput Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\nOther 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
\nData Collection Method by dataset:

Unknown

Labeling Method by dataset:

Unknown

Properties:
\nRoughly 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)
\nTest 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.

\n
Install the NGC CLI from ngc.nvidia.com
\n
\n
Configure the NGC CLI using the following command
\n

ngc config set\n

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

ngc registry model list nvidia/tao/pretrained_efficientdet:*\n

To download the model:

--dest \n\">

ngc registry model download-version nvidia/tao/pretrained_efficientdet:<template> --dest <path>\n

Instructions to run the sample notebook

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

ngc config set\n

Download the sample notebooks from NGC using the command below

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

Invoke the jupyter notebook using the following command

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

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\n

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

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

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

Technical blogs

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Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper.
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2.
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

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.

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This model is ready for commercial use.\n\n## References:\n### Citations\n- 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.\n\n### Other TAO Pre-trained Models \n\n- Get [TAO Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_object_detection) pre-trained models for **YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet** architectures from NGC model registry\n- Get [TAO EfficientDet Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_efficientdet) pre-trained models for **DetectNet_v2** architecture from NGC model registry\n- Get [TAO classification](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_classification) pre-trained models from NGC model registry\n- Get [TAO Instance segmentation](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_instance_segmentation) pre-trained models for **MaskRCNN** architecture from NGC\n- Get [TAO Semantic segmentation](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_semantic_segmentation) pre-trained models for **UNet** architecture from NGC\n- Get Purpose-built models from NGC model registry:\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:facenet)\n - [2D Body Pose Net](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n\n## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf)
\n\nThe models in this instance are feature extractors based on the EfficientNet architecture.\n\n\n## Input:\n**Input Type(s):** Image
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 3D
\n**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.
\n\n## Output:\n**Output Type(s):** Label(s), Bounding-Box(es), Confidence Scores
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\n**Other Properties Related to Output:** Category Label(s): (Labels of object detected), Bounding Box Coordinates, Confidence Scores
\n\n## Software Integration:\n**Runtime Engine(s):** \n* DeepStream 6.1 or later
\n* TAO - 5.2
\n\n\n**Supported Hardware Architecture(s):**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \nThe following efficientnet-x backbone versions are supported in TAO Toolkit:\n\n- efficientnet-b0\n- efficientnet-b1\n- efficientnet-b2\n- efficientnet-b3\n- efficientnet-b4\n- efficientnet-b5\n\n# Training & Evaluation: \n\n## Training Dataset:\n**Link:** [https://github.com/openimages/dataset/blob/main/READMEV3.md](https://github.com/openimages/dataset/blob/main/READMEV3.md)
\n**Data Collection Method by dataset:**
\n* Unknown
\n\n**Labeling Method by dataset:**
\n* Unknown
\n\n**Properties:**
\nRoughly 400,000 images and 7,000 validation images across thousands of classes as defined by [Google OpenImages Version Three (3) dataset](https://github.com/openimages/dataset/blob/main/READMEV3.md). 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](http://play.google.com/store/apps/details?id=com.google.android.apps.village.boond), [g.co/imagelabeler](http://g.co/imagelabeler).\n\n\n## Evaluation Dataset:\n**Link:** [https://github.com/openimages/dataset/blob/main/READMEV3.md](https://github.com/openimages/dataset/blob/main/READMEV3.md)
\n\n**Data Collection Method by dataset:**
\n* Unknown
\n\n**Labeling Method by dataset:**
\n* Unknown
\n\n**Properties:**
\n- 15,000 test images from [Google OpenImages Version Three (3) dataset](https://github.com/openimages/dataset/blob/main/READMEV3.md).\n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- Jetson AGX Xavier\n- Xavier NX\n- Orin\n- Orin NX\n- NVIDIA T4\n- Ampere GPU\n- A2\n- A30\n- L4\n- T4\n- DGX H100\n- DGX A100\n- DGX H100\n- L40\n- JAO 64GB\n- Orin NX16GB\n- Orin Nano 8GB\n\n## How to Use this Model\n\n### Running EfficientDet Models Using TAO \n\nThe 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.\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\n1. Install the NGC CLI from ngc.nvidia.com\n\n2. Configure the NGC CLI using the following command\n\n ```sh\n ngc config set\n ```\n\n3. To view all the backbones that are supported by object detection architecture in TAO:\n\n ```sh\n ngc registry model list nvidia/tao/pretrained_efficientdet:*\n ```\n\n4. To download the model:\n\n ```sh\n ngc registry model download-version nvidia/tao/pretrained_efficientdet: