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https://github.com/sunitroy2703/tensorflow-lite-kotlin-samples
📌This repo contains the kotlin implementation of TensorflowLite Example Android Apps🚀
https://github.com/sunitroy2703/tensorflow-lite-kotlin-samples
android example hacktoberfest kotlin tensorflow tensorflow-examples tensorflow-lite tflite
Last synced: 5 days ago
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📌This repo contains the kotlin implementation of TensorflowLite Example Android Apps🚀
- Host: GitHub
- URL: https://github.com/sunitroy2703/tensorflow-lite-kotlin-samples
- Owner: SunitRoy2703
- License: apache-2.0
- Created: 2021-09-11T13:43:23.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-04-16T16:12:05.000Z (over 2 years ago)
- Last Synced: 2024-11-12T17:03:58.104Z (5 days ago)
- Topics: android, example, hacktoberfest, kotlin, tensorflow, tensorflow-examples, tensorflow-lite, tflite
- Language: Kotlin
- Homepage: https://sunitroy2703.github.io/Tensorflow-lite-kotlin-samples
- Size: 7.29 MB
- Stars: 48
- Watchers: 3
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# TensorflowLite Examples Kotlin
[](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/issues) [](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/network) [](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/stargazers)
This repo contains the kotlin implementation of TensorflowLite Example Apps [here](https://github.com/tensorflow/examples/tree/master/lite/examples), which are mostly implemented in java rightnow.
So if you like to see the kotlin, you can go through the repo!
**Star ⭐️ this repo to support the project!**
**Congrats the project got accepted to [Google Dev Library](https://devlibrary.withgoogle.com/products/ml/repos/SunitRoy2703-Tensorflow-lite-kotlin-samples) 🎉🎉**
# Example apps ⭐️
# Bert QnA
This is an end-to-end example of BERT Question & Answer application built with TensorFlow 2.0, and tested on SQuAD dataset.
Developed by: [Dhruv Nagarajan](https://github.com/dhruvnagarajan)
## Depth Estimation
An Android app which uses the MiDaS model to perform monocular depth estimation on RGB images directly. The app displays a depth map over the live camera feed and works for both the front and the rear cameras.
Contributed from: [this repo](https://github.com/shubham0204/Realtime_MiDaS_Depth_Estimation_Android)
## Digit Classifier
*End-to-end sample of a digit classifier model built with TensorFlow 2.0 (Keras API), and trained on MNIST dataset.*
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Image Segmentation
The used model, [DeepLab](https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.html) is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g. person, dog, cat) to every pixel in the input image.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Optical Character Recognition
OCR is the process of recognizing characters from images using computer vision and machine learning techniques. This reference app demos how to use TensorFlow Lite to do OCR. It uses a text detection model and a text recognition model as a pipeline to recognize texts.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Pose Estimation
This is an app that continuously detects the body parts in the frames seen by your device's camera. These instructions walk you through building and running the demo on an Android device. In this Camera captures are discarded immediately after use, nothing is stored or saved.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## PoseNet
This is an app that continuously detects the body parts in the frames seen by your device's camera. These instructions walk you through building and running the demo on an Android device. Camera captures are discarded immediately after use, nothing is stored or saved.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Recommedation :collision:
This application built with TensorFlow 2.0, and the model is trained based on the public MovieLens dataset. The dataset and model is used for research purpose.
Developed by: [Dhruv Nagarajan](https://github.com/dhruvnagarajan)
## Sound part :musical_note:
This Android application demonstrates how to classify sound on-device. It uses:
- TFLite Task Library
- YAMNet, an audio event classification model.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Style Transfer:high_brightness:
Artistic style transfer is an optimization technique used to take two images: a content image and a style reference image (such as an artwork by a famous painter) and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.
Added from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)
## Text classification :books:
This is an end-to-end example of movie review sentiment classification built with TensorFlow 2.0 (Keras API), and trained on IMDB dataset. The demo app processes input movie review texts, and classifies its sentiment into negative (0) or positive (1).
Developed by: [Sunit Roy](https://github.com/SunitRoy2703)
## :bookmark: Zero DCE (incomplete)
Zero-Reference Deep Curve Estimation or Zero-DCE formulates low-light image enhancement as the task of estimating an image-specific tonal curve with a deep neural network.
Developed by: [Sunit Roy](https://github.com/SunitRoy2703)
## :pushpin: Coming Soon!
**:paperclip:On Device Training**
**:paperclip:Speech commands**
**:paperclip:Smart reply**
**:paperclip:Object detection**
**:paperclip:Model personalization**
**:paperclip:Super resolution**
**:paperclip:Gesture detection**
**:paperclip:Image classification**
**:paperclip:Reinforcement learning**
## Goals📝
- [x] Adding all pre-existing example apps to the repo
- [ ] Designing & Creating other apps using the new [Task API](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview)
- [ ] Designing & Creating example apps with the [Interpreter](https://www.tensorflow.org/lite/inference_with_metadata/lite_support), to show the implementation.
- [ ] Maintaining the Apps
## :collision:Contribute 🤝
### :wave: Contributions are welcome, checkout [contribution guidelines](./CONTRIBUTING.md) :memo:
### :smiling_imp: Join our discord channel to discuss about the project:
### :muscle:Email : [email protected]
## Contributors :eyes:
## License
[Apache License 2.0](LICENSE)