metadata

title: Object Detection
emoji: 🖼
colorFrom: green
colorTo: purple
sdk: gradio
sdk_version: 5.5.0
app_file: app.py
pinned: false
short_description: Object detection via Gradio

Object detection

Aim: AI-driven object detection (on COCO image dataset)

Machine learning models:

facebook/detr-resnet-50,
facebook/detr-resnet-101,
hustvl/yolos-tiny,
hustvl/yolos-small

1. Execution via command line

1.1. Use of torch library

python detect_torch.py

1.2. Use of transformers library

python detect_transformers.py

1.3. Use of HuggingFace pipeline library

python detect_pipeline.py

2. Execution via User Interface

Use of Gradio library for web interface

Command line:

python app.py

Note: The Gradio app should now be accessible at http://localhost:7860

3. Execution via Gradio client API

Note: Use of existing Gradio server (running locally, in a Docker container, or in the cloud as a HuggingFace space or AWS)

3.1. Creation of docker container

Command lines:

sudo docker build -t gradio-app .

sudo docker run -p 7860:7860 gradio-app

The Gradio app should now be accessible at http://localhost:7860

3.2. Direct inference via API

Command line:

python inference_API.py

4. Deployment on Hugging Face

This web application is available on Hugging Face, via a Gradio space

URL: https://huggingface.co/spaces/cvachet/object_detection_gradio

5. Deployment on Docker Hub

This web application is available as a container on Docker Hub

URL: https://hub.docker.com/r/cvachet/object-detection-gradio

Spaces:

cvachet
/

object_detection_gradio

Running