{"id":14959054,"url":"https://github.com/sunitroy2703/tensorflow-lite-kotlin-samples","last_synced_at":"2025-09-09T21:29:17.566Z","repository":{"id":44581168,"uuid":"405389854","full_name":"SunitRoy2703/Tensorflow-lite-kotlin-samples","owner":"SunitRoy2703","description":"📌This repo contains the kotlin implementation of TensorflowLite Example Android 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TensorflowLite Examples Kotlin\n\n![TFLite kotlin samples-3](https://user-images.githubusercontent.com/67560900/136672009-78d66df5-a8cd-42c5-bf4b-c2efc60e8032.png)\n\n[![GitHub issues](https://img.shields.io/github/issues/SunitRoy2703/Tensorflow-lite-samples-kotlin?style=for-the-badge)](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/issues) [![GitHub forks](https://img.shields.io/github/forks/SunitRoy2703/Tensorflow-lite-samples-kotlin?color=gree\u0026style=for-the-badge)](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/network) [![GitHub stars](https://img.shields.io/github/stars/SunitRoy2703/Tensorflow-lite-samples-kotlin?color=orange\u0026style=for-the-badge)](https://github.com/SunitRoy2703/Tensorflow-lite-samples-kotlin/stargazers)\n  \u003cbr/\u003e\u003cbr/\u003e \u003cbr\u003e\nThis 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.\nSo if you like to see the kotlin, you can go through the repo!\n\n**Star ⭐️ this repo to support the project!**\n\n**Congrats the project got accepted to [Google Dev Library](https://devlibrary.withgoogle.com/products/ml/repos/SunitRoy2703-Tensorflow-lite-kotlin-samples) 🎉🎉**\n\n# Example apps ⭐️\n\n# Bert QnA\n\nThis is an end-to-end example of BERT Question \u0026 Answer application built with TensorFlow 2.0, and tested on SQuAD dataset.\n\nDeveloped by: [Dhruv Nagarajan](https://github.com/dhruvnagarajan)\n\n## Depth Estimation\n\nAn 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.\n\nContributed from: [this repo](https://github.com/shubham0204/Realtime_MiDaS_Depth_Estimation_Android)\n\n## Digit Classifier\n*End-to-end sample of a digit classifier model built with TensorFlow 2.0 (Keras API), and trained on MNIST dataset.*\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Image Segmentation\nThe 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.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Optical Character Recognition\nOCR 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.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Pose Estimation\nThis 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.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n\n## PoseNet\nThis 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.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Recommedation  :collision:\nThis 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. \n\nDeveloped by: [Dhruv Nagarajan](https://github.com/dhruvnagarajan)\n\n## Sound part :musical_note:\nThis Android application demonstrates how to classify sound on-device. It uses:\n\n- TFLite Task Library\n- YAMNet, an audio event classification model.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Style Transfer:high_brightness:\nArtistic 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.\n\nAdded from: [this repo](https://github.com/tensorflow/examples/tree/master/lite/examples)\n\n## Text classification :books:\nThis 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).\n\nDeveloped by: [Sunit Roy](https://github.com/SunitRoy2703)\n\n## :bookmark: Zero DCE (incomplete) \n\nZero-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.\n\nDeveloped by: [Sunit Roy](https://github.com/SunitRoy2703)\n\n## :pushpin: Coming Soon! \n\n**:paperclip:On Device Training**\n\n**:paperclip:Speech commands**\n\n**:paperclip:Smart reply**\n\n**:paperclip:Object detection**\n\n**:paperclip:Model personalization**\n\n**:paperclip:Super resolution**\n\n**:paperclip:Gesture detection**\n\n**:paperclip:Image classification**\n\n**:paperclip:Reinforcement learning**\n\n## Goals📝\n - [x] Adding all pre-existing example apps to the repo\n - [ ] Designing \u0026 Creating other apps using the new [Task API](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview)\n - [ ] Designing \u0026 Creating example apps with the [Interpreter](https://www.tensorflow.org/lite/inference_with_metadata/lite_support), to show the implementation.\n - [ ] Maintaining the Apps\n\n ## :collision:Contribute 🤝\n ### :wave: Contributions are welcome, checkout [contribution guidelines](./CONTRIBUTING.md) :memo:\n \n ### :smiling_imp: Join our discord channel to discuss about the project:\n \u003ca href=\"https://discord.gg/SBRfdXs7qD\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/67560900/136423481-be79b2dd-9848-4171-8911-19295a3adc7c.png\" width=\"80\"\u003e\u003c/a\u003e\n\n### :muscle:Email : iamsunitroy03@gmail.com\n \n ## Contributors :eyes:\n ![](https://contrib.rocks/image?repo=SunitRoy2703/Tensorflow-lite-kotlin-samples)\n \n ## License\n\n[Apache License 2.0](LICENSE)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsunitroy2703%2Ftensorflow-lite-kotlin-samples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsunitroy2703%2Ftensorflow-lite-kotlin-samples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsunitroy2703%2Ftensorflow-lite-kotlin-samples/lists"}