install.ipynb

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    "colab": {
      "name": "Publishing.ipynb",
      "provenance": []
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaUG3D6m_FEY"
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      "source": [
        "### **Clone and install the Tensorflow Object Detection API** \n",
        "\n",
        "In order to use the TensorFlow Object Detection API, we need to clone it's GitHub Repo.\n",
        "<br>\n",
        "\n",
        "#### **Dependencies**\n",
        "\n",
        "\n",
        "Most of the dependencies required come preloaded in Google Colab.  No extra installation is needed.\n",
        "<br>\n",
        "\n",
        "#### **Protocol Buffers**\n",
        "\n",
        "\n",
        "\n",
        "The TensorFlow Object Detection API relies on what are called `protocol buffers` (also known as `protobufs`). Protobufs are a language neutral way to describe information. That means you can write a protobuf once and then compile it to be used with other languages, like Python, Java or C [5].\n",
        "\n",
        "The `protoc` command used below is compiling all the protocol buffers in the `object_detection/protos` folder for Python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbkuAjhU-3e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9686f0c2-79ec-4b0e-9ed4-aa778dbba185"
      },
      "source": [
        "!git clone https://github.com/tensorflow/models.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 12, done.\u001b[K\n",
            "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 50253 (delta 3), reused 9 (delta 0), pack-reused 50241\u001b[K\n",
            "Receiving objects: 100% (50253/50253), 559.90 MiB | 20.79 MiB/s, done.\n",
            "Resolving deltas: 100% (34186/34186), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukzhAgID_gyP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6baa1929-d2c7-4aa0-9962-c2a11cceb4ab"
      },
      "source": [
        "%cd /content/models/research/\r\n",
        "!protoc object_detection/protos/*.proto --python_out=.\r\n",
        "# Install TensorFlow Object Detection API.\r\n",
        "!cp object_detection/packages/tf2/setup.py .\r\n",
        "!python -m pip install ."
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n",
            "Successfully installed apache-beam-2.27.0 avro-python3-1.10.1 dill-0.3.1.1 fastavro-1.2.3 future-0.18.2 hdfs-2.5.8 lvis-0.5.3 mock-2.0.0 object-detection-0.1 opencv-python-headless-4.5.1.48 pbr-5.5.1 py-cpuinfo-7.0.0 pyarrow-2.0.0 pyyaml-5.3.1 requests-2.25.1 sentencepiece-0.1.95 seqeval-1.2.2 tensorflow-model-optimization-0.5.0 tf-models-official-2.4.0 tf-slim-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkZtxPYCXLHu"
      },
      "source": [
        "Run the model builder test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzfZSmpSXMxS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "597fac0a-188e-4ee8-b0e6-c39f7ad31033"
      },
      "source": [
        "!python /content/models/research/object_detection/builders/model_builder_tf2_test.py"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "I0113 21:31:21.168457 140501804394368 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "I0113 21:31:21.169473 140501804394368 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 20 tests in 35.358s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}