I don’t know your domain but e.g. if you want an high level API example for the object detection API in model garden we have:

  

      github.com
  

  
    tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb 7


    {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rOvvWAVTkMR7"
      },
      "source": [
        "# Eager Few Shot Object Detection Colab\n",
        "\n",
        "Welcome to the Eager Few Shot Object Detection Colab --- in this colab we demonstrate fine tuning of a (TF2 friendly) RetinaNet architecture on very few examples of a novel class after initializing from a pre-trained COCO checkpoint.\n",
        "Training runs in eager mode.\n",
        "\n",
        "Estimated time to run through this colab (with GPU): \u003c 5 minutes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {



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For TFLite

  

      github.com
  

  
    tensorflow/models/blob/master/research/object_detection/colab_tutorials/eager_few_shot_od_training_tflite.ipynb 4


    {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOvvWAVTkMR7"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "Welcome to the **Few Shot Object Detection for TensorFlow Lite** Colab. Here, we demonstrate fine tuning of a SSD architecture (pre-trained on COCO) on very few examples of a *novel* class. We will then generate a (downloadable) TensorFlow Lite model for on-device inference.\n",
        "\n",
        "**NOTE:** This Colab is meant for the few-shot detection use-case. To train a model on a large dataset, please follow the [TF2 training](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_training_and_evaluation.md#training) documentation and then [convert](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md) the model to TensorFlow Lite."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3U2sv0upw04O"
      },



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