Car Detection and Tracking - Deployment

Edge Impulse Experts / Car Detection and Tracking Public

Target: Raspberry Pi 4 Clone this project

Configure your deployment

You can deploy your impulse to any device. This makes the model run without an internet connection, minimizes latency, and runs with minimal power consumption. Read more.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson's GPUs (JetPack 4.6.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson:bfc3b315fd289582242b19120237810ed1c6bb98

Arguments:

--api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 --run-http-server 1337 --impulse-id 2

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson:bfc3b315fd289582242b19120237810ed1c6bb98 \
        --api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 \
        --run-http-server 1337 \
        --impulse-id 2

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 5.1.x), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:5099e21de99ef77b3aee1e7a664df8849b488c75

Arguments:

--api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 --run-http-server 1337 --impulse-id 2

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:5099e21de99ef77b3aee1e7a664df8849b488c75 \
        --api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 \
        --run-http-server 1337 \
        --impulse-id 2

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Run your model as a Docker container

To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 6.0), use:

Container:

public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:cefd80d921502fe00e1b0091ee6d1aad56b93c25

Arguments:

--api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 --run-http-server 1337 --impulse-id 2

Ports to expose:

1337

For example, in a one-liner locally:

docker run --rm -it --runtime=nvidia --gpus all \
    -p 1337:1337 \
    public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:cefd80d921502fe00e1b0091ee6d1aad56b93c25 \
        --api-key ei_f2f49443d028a8bc41e77c35b9ff488477c3db147d9a67f8b559d582acf45b84 \
        --run-http-server 1337 \
        --impulse-id 2

This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at http://localhost:1337 with instructions.

Read the docs for information, like bundling in your model inside the container and selecting extra hardware optimizations.

Model Optimizations

Model optimizations can increase on-device performance but may reduce accuracy.

Unoptimized (float32)


Latency
Ram
Flash
Accuracy

Image	Object detection	Total
1 ms.	386 ms.	387 ms.
4.0K	N/A	4.0K
-	11.0M	-
		60.00%

Estimate for Raspberry Pi 4.

Clone this project to deploy this impulse.

Configure your deployment

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output

Configure your deployment

Default Deployment

C++ library

Deploy to any Linux-based development board

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Run your model as a Docker container

Model Optimizations

EON™ Compiler

EON™ Compiler

TensorFlow Lite

Unoptimized (float32)

Latest build

Run this model

Build output

Model testing output