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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 | |