validation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Publishing.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGtfi97VCMOp"
      },
      "source": [
        "Here we're going to run the code through a loop that waits for checkpoints to evaluate. Once the evaluation finishes, you're going to see the message:\n",
        "\n",
        "`INFO:tensorflow:Waiting for new checkpoint at /content/training/`\n",
        "\n",
        "Then you can stop the cell\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR_ASWHFRvEt",
        "outputId": "0d15f083-bf96-4090-c178-06a7ed4205dc"
      },
      "source": [
        "!python /content/models/research/object_detection/model_main_tf2.py \\\r\n",
        "    --pipeline_config_path={pipeline_config_path} \\\r\n",
        "    --model_dir={model_dir} \\\r\n",
        "    --checkpoint_dir={model_dir}  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Performing evaluation on 89 images.\n",
            "I1220 17:34:19.304409 139900847355776 coco_evaluation.py:293] Performing evaluation on 89 images.\n",
            "creating index...\n",
            "index created!\n",
            "INFO:tensorflow:Loading and preparing annotation results...\n",
            "I1220 17:34:19.304893 139900847355776 coco_tools.py:116] Loading and preparing annotation results...\n",
            "INFO:tensorflow:DONE (t=0.00s)\n",
            "I1220 17:34:19.308917 139900847355776 coco_tools.py:138] DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.42s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.05s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.222\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.405\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.221\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.239\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.293\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.414\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.067\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.543\n",
            "INFO:tensorflow:Eval metrics at step 7000\n",
            "I1220 17:34:19.793375 139900847355776 model_lib_v2.py:954] Eval metrics at step 7000\n",
            "INFO:tensorflow:\t+ Loss/localization_loss: 0.345804\n",
            "INFO:tensorflow:\t+ Loss/classification_loss: 1.496982\n",
            "INFO:tensorflow:\t+ Loss/regularization_loss: 0.130125\n",
            "INFO:tensorflow:\t+ Loss/total_loss: 1.972911\n",
            "INFO:tensorflow:Waiting for new checkpoint at /content/training/\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}

I want to know that the AP did not exceed 50%. Is this normal?