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README.md

TensorFlow.js Example: Simple Object Detection

This example illustrates how to train a model to perform simple object detection in TensorFlow.js. It includes the full workflow:

  • Generation of synthetic images and labels for training and testing
  • Creation of a model for the object-detection task based on a pretrained computer-vision model (MobileNet)
  • Training of the model in Node.js using tfjs-node
  • Transfering the model from the Node.js environment into the browser through saving and loading
  • Performing inference with the loaded model in the browser and visualizing the inference results.

How to use this example

First, train the model using Node.js:

yarn
yarn train

Then, run the model in the browser:

yarn watch

Adjusting training parameters from the "yarn train" command line

The yarn train command stores all training examples in memory. Hence, it is possible for the process to run out of memory and crash if there are too many training examples generated. In the meantime, having a large number of training examples benefits the accuracy of the model after training. The default number of examples is 2000. You can adjust the number of examples by using the --numExamples flag of the yarn train command. For example, the hosted model is trained with the 20000 examples, using the command line:

yarn train \
    --numExamples 20000 \
    --initialTransferEpochs 100 \
    --fineTuningEpochs 200

See train.js for other adjustable parameters.

Using CUDA GPU for Training

Note that by default, the model is trained using the CPU version of tfjs-node. If you machine is equipped with a CUDA(R) GPU, you may switch to using tfjs-node-gpu, which will significantly shorten the training time. Specifically, add the --gpu flag to the command above, i.e.,

yarn train --gpu \
    --numExamples 20000 \
    --initialTransferEpochs 100 \
    --fineTuningEpochs 200

See this example live!

Monitoring model training with TensorBoard

The Node.js-based training script allows you to log the loss values to TensorBoard. Compared to printing loss values to the console, which the training script performs by default, logging to tensorboard has the following advantages:

  1. Persistence of the loss values, so you can have a copy of the training history available even if the system crashes in the middle of the training for some reason, while logs in consoles are more ephemeral.
  2. Visualizing the loss values as curves makes the trends easier to see (e.g., see the screenshot below).

To do this in this example, add the flag --logDir to the yarn train command, followed by the directory to which you want the logs to be written, e.g.,

yarn train
   --numExamples 20000 \
   --initialTransferEpochs 100 \
   --fineTuningEpochs 200 \
   --logDir /tmp/simple-object-detection-logs

Then install tensorboard and start it by pointing it to the log directory:

# Skip this step if you have already installed tensorboard.
pip install tensorboard
tensorboard --logdir /tmp/simple-object-detection-logs

tensorboard will print an HTTP URL in the terminal. Open your browser and navigate to the URL to view the loss curves in the Scalar dashboard of TensorBoard.