The Android app which uses the hand tracking model from this repo is available here -> shubham0204/Hand_Detection_TFLite_AndroidĪlso, a step-by-step guide on how to convert the model checkpoints to a TFLite model (. The trained model checkpoints are converted to the TensorFlow Lite format so that they can used in both Android and iOS apps.
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Update - Android example using a TFLite model Live Demo : Handtrack.js Examples in the Browser.Blog Post: Hand Tracking Interactions in the Browser using Tensorflow.js and 3 lines of code.You can do hand tracking in 3 lines of code, no installation, no model training, all in the browser. I exported the model using the Tensorflow.js converter and have it wrapped into an easy to use javascript library - Handtrack.js. P.S if you are using or have used the models provided here, feel free to reach out on twitter ( and share your work! Update 3/5/19 - You can now use the model in the Browser using Handtrack.js Using the Detector to Detect/Track hands.Data preparation and network training in Tensorflow (Dataset, Import, Training).Motivation - Why Track/Detect hands with Neural Networks.model-checkpoint/ssd_mobilenet_v1_nfig \ You can copy it to the current directory and use it as follows
The tensorflow object detection repo has a python file for exporting a checkpoint to frozen graph here. You may need to generate your own frozen model graph using the model checkpoints in the repo to fit your TF version.
Using a different version may result in some errors. Note: The code in this repo is written and tested with Tensorflow 1.4.0-rc0. Run while visualizing results (image above) īoth examples above were run on a macbook pro CPU (i7, 2.5GHz, 16GB). Realtime detection on video stream from a webcam. If you use this tutorial or models in your research or project, please cite this. Better still, provide code that can be adapted to other uses cases. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). I then tried the Egohands Dataset which was a much better fit to my requirements. I experimented first with the Oxford Hands Dataset (the results were not good). I was interested mainly in detecting hands on a table (egocentric view point). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). Real-time Hand-Detection using Neural Networks (SSD) on Tensorflow.
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