From dfbbc9d6dbeadf06734b2f89b26b8a76c8c990a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 16 Apr 2020 09:36:09 -0700
Subject: [PATCH] Add OSS template for OVIC winners.

PiperOrigin-RevId: 306862647
Change-Id: I53ef3bfba7ccc8539471b255473dd30050e31b32
---
 .../lite/java/ovic/Winner_OSS_Template.md     | 121 ++++++++++++++++++
 1 file changed, 121 insertions(+)
 create mode 100644 tensorflow/lite/java/ovic/Winner_OSS_Template.md

diff --git a/tensorflow/lite/java/ovic/Winner_OSS_Template.md b/tensorflow/lite/java/ovic/Winner_OSS_Template.md
new file mode 100644
index 00000000000..6716aac456e
--- /dev/null
+++ b/tensorflow/lite/java/ovic/Winner_OSS_Template.md
@@ -0,0 +1,121 @@
+<!--
+• This is a README.md template we encourage you to use when you release your model.
+• There are general sections we added to this template for various ML models.
+• You may need to add or remove sections depending on your needs.
+-->
+
+# Project Name
+
+## Authors
+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/))
+
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+
+## Description
+<!-- Provide description of the model -->
+The model submitted for the OVIC and full implementation code for training, evaluation, and inference
+
+* OVIC track: Image Classification, Object Detection
+
+## Algorithm
+<!-- Provide details of the algorithms used -->
+
+## Requirements
+<!--
+• Provide description of the model 
+• Provide brief information of the algorithms used
+-->
+
+To install requirements:
+
+```setup
+pip install -r requirements.txt
+```
+
+## Pre-trained Models
+
+| Model | Download | MD5 checksum |
+|-------|----------|--------------|
+| Model Name | Download Link (Size: KB) | MD5 checksum |
+
+The model tar file contains the followings:
+* Trained model checkpoint
+* Frozen trained model
+* TensorFlow Lite model
+
+## Results
+
+### [4th OVIC Public Ranked Leaderboard](https://lpcvc.ecn.purdue.edu/score_board_r4/?contest=round4)
+
+#### Image Classification (from the Leaderboard)
+
+| Rank | Username | Latency | Accuracy on Classified | # Classified | Accuracy/Time | Metric | Reference Accuracy |
+|------|----------|---------|------------------------|--------------|---------------|--------|--------------------|
+| 1 | Username | xx.x | 0.xxxx | 20000.0 | xxx | 0.xxxxx | 0.xxxxx |
+
+ * **Metric**: Accuracy improvement over the reference accuracy from the Pareto optimal curve
+ * **Accuracy on Classified**: The accuracy in [0, 1] computed based only on the images classified within the wall-time
+ * **\# Classified**: The number of images classified within the wall-time
+ * **Accuracy/Time**: The accuracy divided by either the total inference time or the wall-time, whichever is longer
+ * **Reference accuracy**: The reference accuracy of models from the Pareto optimal curve that have the same latency as the submission
+
+#### Object Detection
+
+| Rank | Username | Metric | Runtime | mAP over time | mAP of processed |
+|------|----------|--------|---------|---------------|------------------|
+| 1 | Username | 0.xxxxx | xxx.x | xxx | xxx |
+
+* **Metric**: COCO mAP computed on the entire minival dataset
+* **mAP over time**: COCO mAP on the minival dataset divided by latency per image
+* **mAP of processed**: COCO mAP computed only on the processed images
+
+## Dataset
+<!--
+• Provide detailed information of the dataset used
+-->
+
+## Training
+<!--
+• Provide detailed training information (preprocessing, hyperparameters, random seeds, and environment) 
+• Provide a command line example for training.
+-->
+
+Please run this command line for training.
+
+```shell
+python3 ...
+```
+
+## Evaluation
+<!--
+• Provide evaluation script with details of how to reproduce results.
+• Describe data preprocessing / postprocessing steps
+• Provide a command line example for evaluation.
+-->
+
+Please run this command line for evaluation.
+
+```shell
+python3 ...
+```
+
+## References
+<!-- Link to references -->
+
+## License
+<!--
+• Place your license text in a file named LICENSE.txt (or LICENSE.md) in the root of the repository.
+• Please also include information about your license in this README.md file.
+e.g., [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)
+-->
+
+This project is licensed under the terms of the **Apache License 2.0**.
+
+## Citation
+<!--
+If you want to make your repository citable, please follow the instructions at [Making Your Code Citable](https://guides.github.com/activities/citable-code/)
+-->
+
+If you want to cite this repository in your research paper, please use the following information.