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---
title: Object Detection ECS
emoji: 🌖
colorFrom: purple
colorTo: green
sdk: gradio
sdk_version: 5.5.0
app_file: app.py
pinned: false
short_description: Object detection ECS
---
# Object detection via ECS endpoints
[](https://www.python.org/downloads/)
[](https://hub.docker.com/repository/docker/cvachet/object-detection-ecs)
**Aim: AI-driven object detection task**
- Front-end: user interface via Gradio library
- Back-end: use of AWS ECS endpoints to run Machine Learning models
----
**Table of contents:**
- [Front-end user interface](#1-front-end-user-interface)
- [Environment variables](#11-environment-variables)
- [Local execution](#12-local-execution)
- [Deployment on Hugging Face](#13-deployment-on-hugging-face)
- [Back-end Machine Learning models](#2-back-end-machine-learning-models)
- [Information on ML models](#21-information-on-ml-models)
- [Deployment on AWS ECS](#22-information-on-aws-ecs-deployment)
----
## 1. Front-end user interface
### 1.1. Environment variables
This web app uses two environment variables, corresponding to the endpoints for the deployed machine learning models
(cf. [Section 2 - Back-end ML models](#2-back-end-ml-models))
Environment variables:
- AWS_DETR_URL: endpoint for DETR model
- AWS_YOLOS_URL: endpoint for YOLOS model
### 1.2. Local execution
Use of Gradio library for web interface
Command line:
> python3 app.py
<b>Note:</b> The Gradio app should now be accessible at http://localhost:7860
### 1.3. Deployment on Hugging Face
This web application has been deployed on Hugging Face.
HF Space URL: https://huggingface.co/spaces/cvachet/object_detection_ecs
## 2. Back-end machine learning models
Machine Learning (ML) models are available on Docker Hub and have been deployed to AWS ECS (Elastic Container Service)
### 2.1. Information on ML models
**Github repos:**
- DETR API: https://github.com/clementsan/object_detection_detr_api
- YOLOS API: https://github.com/clementsan/object_detection_yolos_api
**Docker hub containers:**
- DETR API: https://hub.docker.com/r/cvachet/object-detection-detr-api
- YOLOS API: https://hub.docker.com/r/cvachet/object-detection-yolos-api
### 2.2 Information on AWS ECS deployment
ECS: Elastic Container Service
<details>
Steps after docker images are available on Docker Hub
### Step 1. Create a new ECS task definition
- Task name (e.g. ObjectDetectionDETRTask)
- Infrastructure requirement:
- Launch type: ```AWS Fargate```
- Architecture: ```Linux/X86_64```
- Task size: ```0.5 CPU, 3GB memory```
- Container:
- Container name: (e.g. ```object-detection-detr```)
- Image uri: point to Docker image URI (e.g. ```cvachet/object-detection-detr-api```)
- Port mapping: assess port number (e.g. ```port 8000, TCP protocol```)
### Step 2. Create a new ECS cluster
- Cluster name (e.g. ```ObjectDetectionCluster```)
### Step 3. Add a new service to the cluster
- Compute configuration
- Use capacity provider strategy (e.g. using Fargate or Fargate_spot)
- Deployment configuration
- Application Type: Service
- Task Family: Select task definition family from prior instance (e.g. ```ObjectDetectionDETRTask```)
- Assign a Service Name: (e.g. ```object-detection-detr-api```)
### Step 4. Update security group for new service
- Go to Cluster -> service -> task -> configuration and networking
- Click on ```Security Group```
- Adjust rules for inbound traffic (e.g. traffic only from my_ip)
</details>
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