We would love your help! You can help by creating a TensorFlow Lite (tflite/TFLite) model ready for implementation, add a mobile app idea that needs a tflite model created, or write an end-to-end tutorial with sample code.
This is also where you can seek help from the community.
If you are interested in helping out, take a look at the potential projects below and assign an corresponding issue to yourself from the repo issue list.
Once you form a project team, move the idea to the "in progress" section below, create a new repo and link to it.
- YOLO - overview.
- Classify pose - overview (an example use-case can be found here).
- A mobile application (preferably Android/iOS) demonstrating optical character recognition (refer to this Colab Notebook to see how this is done in Python).
- A mobile application (preferably Android/iOS) demonstrating several text-to-speech models as shown in this repository.
Here are some more details on how exactly you can help:
- Generate ideas
- Create tflite model(s)
- Create Colab Notebook(s) demonstrating the model creation process along with running inference in Python
- Publish tflite model(s) optional
- Develop Android app(s) to demonstrate the model(s)
- Develop iOS app(s) to demonstrate the model(s)
The tutorials listed below would give you a good idea of the afore-mentioned pointers. The Contribution Guidelines provide detailed instructions on how you can contribute.
Take a look at the in progress projects to see what it's like to work on a project.
- DeepSpeech - a very popular ASR framework - project repo.
- Enhanced super res GAN - project repo.
- Speech Command - overview.
Once a project has been completed, please open a PR to awesome-tfite to add the links of the tflite model, sample code and tutorials.
- U-GAT-IT (Selfie <-> Anime) - project repo.
- SPICE (Pitch Detection) - Project repo - Medium article.
- How to Create a Cartoonizer with TensorFlow Lite - project repo, blog post.
- Optimizing MobileDet for Mobile Deployments - Colab Notebook, article.
- Training custom object detectors and converting them to TFLite - project repo. This repository shows how to train a custom detection model with the TFOD API (TF2 and TF1), optimize it with TFLite, and perform inference with the optimized model.
- Create Artistic Effect by Stylizing the Image Background - Part I | Part II | Part III | Code Repository.
- Text detectors in TensorFlow Lite - Converting CRAFT to TFLite: A Guide to PyTorch-TFLite Conversion | A Battle of Text Detectors for Mobile Deployments: CRAFT vs. EAST.
- OCR TFLite Models - Project Repository | End-to-End OCR Notebook.
- Sound classification - Overview | Project repo | Tutorial.
- MIRNet TFLite models for low-light image enhancement - project repository | TFLite models on TF Hub | MIRNet Flutter App Repository.
- Zero-DCE TFLite models for low light image enhancement - project repository | TFLite models on TF Hub
- TTS TFLite Models (Tacotron2, FastSpeech2, MelGAN, MB-MELGAN, PWGAN) - project repo.
- Boundless model for image extrapolation - Colab Notebook | Models on TF-Hub.
- Pose Classification based Video Game Control using TensorFlow Lite - Project Repository by Nitin Tiwari.
This should be done via GitHub issues. Only use it when you have something relevant to discuss otherwise the issues will be automatically closed.