How to Train TensorFlow Lite Object Detection Models Using Google Colab | SSD MobileNet

Let's train, export, and deploy a TensorFlow Lite object detection model on the Raspberry Pi - all through a web browser using Google Colab! We'll walk through a Colab notebook that provides start-to-finish code and instructions for training a custom TFLite model, and then show how to run it on a Raspberry Pi. The notebook uses the TensorFlow Object Detection API to train SSD-MobileNet or EfficientDet models and converts them to TFLite format.

-- Other Links --

🐜 How to quantize your TFLite model: Still to come!

-- Chapters --

0:00 Introduction
1:06 Google Colab
1:41 1. Gather Training Images
3:22 2. Install TensorFlow
4:43 3. Upload Images and Prepare Data
8:41 4. Set up Training Configuration
11:20 5. Train Model
13:48 6. Convert Model to TFLite
14:20 7. Test Model
17:50 8. Deploy Model
22:07 9. Quantization
22:30 Conclusion

-- Music --

- Blue Wednesday – Japanese Garden
- Provided by Lofi Records

[qcfpppp]

Ive been banging my head on my desk trying to find a good tutorial. Big thank

[SAYED-ZALALBIA.]

Thank you for a wonderful breakdown of all the needed steps to do training on colab ❤❤❤ Your follower from Egypt 🥰

[fe_nik_s]

Hello, to increase performance, you can use multithreaded computing. To do this, when loading the model, specify the "num_threads" argument, which must contain the number of threads that the processor supports. Basically, TFLite uses only one core.
The code:
import multiprocessing
tf.lite.Interpreter(model_path=PATH_TO_CKPT, )

On my old laptop with dual-core CPU, this gave a double increase in performance.
Basic - 140 ms per frame.
With two cores - 85 ms per frame

[MakerMadness]

Really excited your back! Thanks for the video.

[ivandimitrov]

Amazing tutorial! One of the most useful videos I've ever watched. Keep up the good work!
Thanks a lot, Edje!

[felixoyeleke]

Your tutorial is the best on the internet. Exactly what I needed! Thank you! Thank you! Thank you!!!

[baumotorola]

Really nice video. Anxiously waiting for an Android deploying tutorial!

[AlexanderSomma]

Thank you for a wonderful breakdown of all the needed steps to do training on colab.

[CRIMXN]

Thank you! Clear and easy to understand while having sprinkles of humor made this video really educational AND enjoyable. Easiest like and subscribe.

[iotlalo]

I struggle a week trying the repo from Google and this video save my life thank you I would like to see a video of posenet in Raspberry Pi

[huberthernandezjr.]

its working edje electronics, i transfer it to raspberry pi also and it did not work at first time i just update some lib and it worked now thankyouuu thankyouuu super awsomeeee

[ejarao]

Still is one of the best tutorial for model training in YouTube. But I have a suggestion, could there be an option to add metadata scripts to the notebook? this could be highly useful since of the release of googles ML kit for android & ios development.

[EdjeElectronics]

Hey all, just a quick update (April 10 2024). I ran through the full notebook today with my coin dataset, and everything worked without errors. A "Restart Session" option appears during Step 1 after running the last set of install commands. When it appears, click the "Restart Session" option, and then keep working through the steps. If you're getting errors, try using my coin dataset and seeing if it works (it should). Then, compare your annotation files with the annotation files from the coin dataset. It's likely there's a difference in your annotation files that are causing problems.

[ujjwalpranami]

Thankyou so much you made my day, I was searching all internet for working colab notebook for this model.

[lucasinunas]

for anyone who noticed that not all images are getting moved to its respective folders, and that later it gives some error that a file is missing: make sure all your images end with .jpg instead of .jpeg since all the scripts on the github page only takes into account those files

[navishkhadooa]

Really amazing video with detailed and well-explained steps. Great job!

[istillplay]

Thankkk youu sooo much for the tutorial, I've been researching on the internet but didn't get the solution. Great work you've earned a subscriber.

[LaraCalvo-mkuh]

This tutorial was super helpful, thank you a lot! It is very easy to follow along and understand with step-by-step instructions

[kai-ten]

Great tutorial!! Great introductory experience while also providing everything needed for learning rabbit holes.

If you come across Step 5 ending with ^c, it is because you are running out of memory. Either resize your images to be large/medium (~200-300KB per image), upload less images, or both.

[Tzu-LingLiu]

Thanks for making this amazing video! It is really helpful. Look forward to the "quantize TFLite model:" video too!!!