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# Object detection | |
Aim: AI-driven object detection (on COCO image dataset) | |
## Direct object detection via python scripts | |
### 1. Use of torch library | |
> python detect_torch.py | |
### 2. Use of transformers library | |
> python detect_transformers.py | |
### 3. Use of HuggingFace pipeline library | |
> python detect_pipeline.py | |
## Object detection via User Interface | |
Use of Gradio library for web interface | |
Command line: | |
> python app.py | |
<b>Note:</b> The Gradio app should now be accessible at http://localhost:7860 | |
## Object detection via Gradio client API | |
<b>Note:</b> Use of existing Gradio server (running locally, in a Docker container, or in the cloud as a HuggingFace space or AWS) | |
### 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 | |
### 2. Direct inference via API | |
Command line: | |
> python inference_API.py | |