How can I load/retrain/save ssd_inception_v2_coco

ML / Tensorflow beginner.

Can any of these already-trained models be loaded on tfjs and re-trained there, then exported to Downloads or is Tensorflow python the only way to go?

I see this process is well described and documented in this tutorial for Tensorflow Python but unfortunately I can't find any documentation/tutorial to re-train an object detection model on the browser with tfjs (image classification yes, object detection no).

I see how I could load the coco-ssd model using npm, then probably even trigger saving it to downloads, but what about:

config file (need to modify it because I want to have only one class, not 90)
annotated images (both .jpg, .xml and .csv)
labels.pbtxt
.record files

Is there any way to go through the process of retraining an ssd model such as ssd_inception_v2_coco and I'm not hitting the right google keywords or is it just not possible in the current state of the framework?

You can use transfer learning by using coco-ssd model as a feature extractor. An example of transfer-learning can be seen here.

Here is a model which extracts features using a features extractor as an input for a new sequential model.

const loadModel = async () => {
  const loadedModel = await tf.loadModel(MODEL_URL)
  console.log(loadedModel)
  // take whatever layer except last output
  loadedModel.layers.forEach(layer => console.log(layer.name))
  const layer = loadedModel.getLayer(LAYER_NAME)
  return tf.model({ inputs: loadedModel.inputs, outputs: layer.output });
}
loadModel().then(featureExtractor => {
  model = tf.sequential({
    layers: [
      // Flattens the input to a vector so we can use it in a dense layer. While
      // technically a layer, this only performs a reshape (and has no training
      // parameters).
      // slice so as not to take the batch size
      tf.layers.flatten(
        { inputShape: featureExtractor.outputs[0].shape.slice(1) }),
      // add all the layers of the model to train
      tf.layers.dense({
        units: UNITS,
        activation: 'relu',
        kernelInitializer: 'varianceScaling',
        useBias: true
      }),
      // Last Layer. The number of units of the last layer should correspond
      // to the number of classes to predict.
      tf.layers.dense({
        units: NUM_CLASSES,
        kernelInitializer: 'varianceScaling',
        useBias: false,
        activation: 'softmax'
      })
    ]
  });
})

To detect a single object out of the 90 classes of coco-ssd, one could simply use a conditional test on the prediction of coco-ssd.

const image = document.getElementById(id)

cocoSsd.load()
  .then(model => model.detect(image))
  .then(prediction => {
if (prediction.class === OBJECT_DETECTED) {
  // display it the bbox to the user}
})

If the class does not exist in coco-ssd, then one needs to builds a detector.