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https://github.com/cloudera/cml_amp_object_detection_inference

Interact with a blog-style Streamlit application to visually unpack the inference workflow of a modern, single-stage object detector.
https://github.com/cloudera/cml_amp_object_detection_inference

computer-vision object-detection pytorch streamlit

Last synced: 3 months ago
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Interact with a blog-style Streamlit application to visually unpack the inference workflow of a modern, single-stage object detector.

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README 

        
# Object Detection Inference: _Visualized_



![](images/collage.png)



This repository offers a blog-style Streamlit application to help visualize the inference workflow of a single-stage object detector. Specifically, we see how a [RetinaNet](https://arxiv.org/abs/1708.02002) architecture processes an image to quickly and accurately detect objects, while also exploring the following fundamental object detection concepts along the way:



- Multi-scale feature extraction with Feature Pyramid Networks (FPNs)

- Inline anchor box generation with Region Proposal Networks (RPNs)

- Detection post-processing with Non-Maximum Suppression (NMS)



Throughout the repo, we utilize a [PyTorch implementation of RetinaNet](https://github.com/pytorch/vision/blob/master/torchvision/models/detection/retinanet.py) that has been pre-trained on the [Common Objects in Context (COCO) 2017](https://cocodataset.org/#home) dataset. 



## Repository Structure



This project is organized with the following directory structure:



```

.

├── cml                           # Scripts that facilitate the project setup on CML

    ├── install_dependencies.py

    └── launch_app.py

├── app                           # Streamlit application files

    ├── SessionState.py

    ├── app.py

    └── app_pages.py

├── src                           # Modules supporting the model, data, and application

    ├── anchor_utils.py

    ├── app_utils.py

    ├── data_utils.py

    ├── model_utils.py

    └── retinanet.py

├── data                           # Storage directory for data assets

├── images

├── LICENSE

├── README.md  

├── requirements.txt 

└── .project-metadata.yaml

```



## Deploying on CML



There are three ways to launch this project on CML:



1. **From Prototype Catalog** - Navigate to the Prototype Catalog on a CML workspace, select the "Object Detection Inference: _Visualized_" tile, click "Launch as Project", click "Configure Project"

2. **As ML Prototype** - In a CML workspace, click "New Project", add a Project Name, select "ML Prototype" as the Initial Setup option, copy in this repo URL, click "Create Project", click "Configure Project"

3. **Manual Setup** - In a CML workspace, click "New Project", add a Project Name, select "Git" as the Initial Setup option, copy in the repo URL, click "Create Project". Launch a Python 3 Workbench Session and run `!pip3 install -r requirements.txt` to install requirements. Then create a CML Application as described in the [CML documentation](https://docs.cloudera.com/machine-learning/1.1/applications/topics/ml-applications.html), using `app/app.py` as the launch script.



## Setup outside of CML



The code and applications within were developed against Python 3.8.6. To setup the application outside of a CML environment,  first create and activate a new virtual environment through your preferred means, then pip install dependencies from the requirements file:



```bash

python3 -m venv .venv

source .venv/bin/activate

pip3 install -r requirements.txt

```



To start the application, run the following command from the root directory of the repo:



```

streamlit run app/app.py

```



**Note -** you may need to configure ports depending on where the application is launched from.