Add OSS template for OVIC winners.
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A. Unique TensorFlower
TensorFlower Gardener
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tensorflow/lite/java/ovic/Winner_OSS_Template.md
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tensorflow/lite/java/ovic/Winner_OSS_Template.md
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<!--
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• This is a README.md template we encourage you to use when you release your model.
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• There are general sections we added to this template for various ML models.
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• You may need to add or remove sections depending on your needs.
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-->
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# Project Name
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## Authors
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The **1st place winner** of the **4th On-device Visual Intelligence Competition** ([OVIC](https://docs.google.com/document/d/1Rxm_N7dGRyPXjyPIdRwdhZNRye52L56FozDnfYuCi0k/edit#)) of Low-Power Computer Vision Challenge ([LPCVC](https://lpcv.ai/))
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* Last name, First name ([@GitHubUsername](https://github.com/username))
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* Last name, First name ([@GitHubUsername](https://github.com/username))
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* Last name, First name ([@GitHubUsername](https://github.com/username))
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## Description
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<!-- Provide description of the model -->
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The model submitted for the OVIC and full implementation code for training, evaluation, and inference
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* OVIC track: Image Classification, Object Detection
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## Algorithm
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<!-- Provide details of the algorithms used -->
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## Requirements
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<!--
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• Provide description of the model
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• Provide brief information of the algorithms used
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-->
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To install requirements:
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```setup
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pip install -r requirements.txt
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```
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## Pre-trained Models
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| Model | Download | MD5 checksum |
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|-------|----------|--------------|
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| Model Name | Download Link (Size: KB) | MD5 checksum |
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The model tar file contains the followings:
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* Trained model checkpoint
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* Frozen trained model
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* TensorFlow Lite model
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## Results
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### [4th OVIC Public Ranked Leaderboard](https://lpcvc.ecn.purdue.edu/score_board_r4/?contest=round4)
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#### Image Classification (from the Leaderboard)
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| Rank | Username | Latency | Accuracy on Classified | # Classified | Accuracy/Time | Metric | Reference Accuracy |
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|------|----------|---------|------------------------|--------------|---------------|--------|--------------------|
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| 1 | Username | xx.x | 0.xxxx | 20000.0 | xxx | 0.xxxxx | 0.xxxxx |
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* **Metric**: Accuracy improvement over the reference accuracy from the Pareto optimal curve
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* **Accuracy on Classified**: The accuracy in [0, 1] computed based only on the images classified within the wall-time
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* **\# Classified**: The number of images classified within the wall-time
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* **Accuracy/Time**: The accuracy divided by either the total inference time or the wall-time, whichever is longer
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* **Reference accuracy**: The reference accuracy of models from the Pareto optimal curve that have the same latency as the submission
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#### Object Detection
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| Rank | Username | Metric | Runtime | mAP over time | mAP of processed |
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|------|----------|--------|---------|---------------|------------------|
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| 1 | Username | 0.xxxxx | xxx.x | xxx | xxx |
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* **Metric**: COCO mAP computed on the entire minival dataset
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* **mAP over time**: COCO mAP on the minival dataset divided by latency per image
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* **mAP of processed**: COCO mAP computed only on the processed images
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## Dataset
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<!--
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• Provide detailed information of the dataset used
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-->
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## Training
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<!--
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• Provide detailed training information (preprocessing, hyperparameters, random seeds, and environment)
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• Provide a command line example for training.
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-->
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Please run this command line for training.
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```shell
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python3 ...
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```
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## Evaluation
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<!--
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• Provide evaluation script with details of how to reproduce results.
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• Describe data preprocessing / postprocessing steps
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• Provide a command line example for evaluation.
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-->
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Please run this command line for evaluation.
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```shell
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python3 ...
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```
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## References
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<!-- Link to references -->
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## License
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<!--
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• Place your license text in a file named LICENSE.txt (or LICENSE.md) in the root of the repository.
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• Please also include information about your license in this README.md file.
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e.g., [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)
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-->
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This project is licensed under the terms of the **Apache License 2.0**.
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## Citation
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<!--
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If you want to make your repository citable, please follow the instructions at [Making Your Code Citable](https://guides.github.com/activities/citable-code/)
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-->
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If you want to cite this repository in your research paper, please use the following information.
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