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Real-time SKU detection in the browser using TensorFlow.js

↗ Quelle (blog.tensorflow.org)
🗣️ Stimme:

Posted by . This received lots of interest from developers from all over the world who tried to apply the solution to their personal or business projects.While answering reader’s questions on my first article, I noticed a few difficulties in adapting our solution to large datasets, and deploying the resulting model in production using the new version of TensorFlow.js.

Therefore, the goal of this article is to share a solution for a well-known problem in the consumer packaged goods (CPG) industry: real-time and offline

and the
format and has to be converted to and the tf.record version of the dataset is also available in my
)

To work with this dataset, the neural network chosen has to be very effective in recognizing patterns and be small enough to run in real-time in TensorFlow.js.

Choosing the model

There are a variety of neural networks capable of solving the SKU detection problem. But, the architectures that easily achieve a high level of precision are very dense and don't have reasonable inference times when converted to TensorFlow.js to run in real-time.

Because of that, the approach here is going to be to focus on optimizing a mid-level neural network to achieve reasonable precision working on densely packed scenes and run the inferences in real-time. Analyzing the that makes it easy to construct, train, and deploy object detection models. In this project, we’re going to use this API and train the model using a.

Setting up the environment

Next, download and extract the dataset using the following commands:

, and they are going to be downloaded using the following commands:

, but the focus here is to train the model to recognize one class so these weights are going to be used only to initialize the network — this technique is known as and performed a sequence of experiments (thanks Google Developers for the free resources) to optimize the model to work with densely packed scenes on the SKU110K dataset. Download the configuration and check the parameters using the code below.

With the parameters set, start the training by executing the following command:

).

The training process was monitored through Tensorboard and took around 22h to finish on a 60GB machine using an NVIDIA Tesla P4. The final losses can be checked below

Total training loss

Validate the model

Now let’s evaluate the trained model using the test data:

: precision, recall, and loss (

(pb) file. This file is going to have the graph definition and the weights of the model.

Deploying the model

The model is going to be deployed in a way that anyone can open a PC or mobile camera and perform inference in real-time through a web browser. To do that, we’re going to convert the saved model to the TensorFlow.js layers format, load the model in a JavaScript application and make everything available on and then open a terminal in the inference-graph folder and create and activate a new virtual environment: 

virtualenv -p python3 venv
source venv/bin/activate

Install the which ops are currently supported on the various backends that TensorFlow.js executes on such as WebGL, WebAssembly, or plain JavaScript.

Model conversion using TensorFlow.js Converter (Full resolution image ). The contents of the web_model folder currently contains the files shown below: 

└ web_model
  ├── group1-shard1of5.bin
  ├── group1-shard2of5.bin
  ├── group1-shard3of5.bin
  ├── group1-shard4of5.bin
  ├── group1-shard5of5.bin
  └── model.json

Configuring the application

The model is ready to be loaded in JavaScript. I’ve created an application to perform inference directly from the browser. Let’s: 

npm install http-server -g

Go to the models folder and run the command below to make the model available at http://127.0.0.1:8080 . This is a good choice when you want to keep the model weights in a safe place and control who can request inferences to it. The -c1 parameter is added to disable caching, and the --cors flag enables).

When loading the model, TensorFlow.js will perform the following requests: 

GET /model.json
GET /group1-shard1of5.bin
GET /group1-shard2of5.bin
GET /group1-shard3of5.bin
GET /group1-shardo4f5.bin
GET /group1-shardo5f5.bin

Publishing in CodeSandbox

as well if you prefer.

Conclusion

Besides the precision, an interesting part of these experiments is the inference time — everything runs in real-time in the browser via JavaScript. SKU detection models running in the browser, .

Thanks for reading!

Acknowledgments

I’d like to thank the, for creating and open-sourcing the dataset used in this project.

Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
↗ Original-Artikel auf blog.tensorflow.org lesen
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