experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Add OSS template for OVIC winners.

Browse Source 
PiperOrigin-RevId: 306862647
Change-Id: I53ef3bfba7ccc8539471b255473dd30050e31b32
This commit is contained in:
A. Unique TensorFlower 2020-04-16 09:36:09 -07:00 committed by TensorFlower Gardener
parent 7c0fea00eb
commit dfbbc9d6db
1 changed files with 121 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

121
tensorflow/lite/java/ovic/Winner_OSS_Template.md Normal file
View File

@ -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.