experiments/results_5-shot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80 classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [24.9, 25.0, 24.9, 25.0, 24.9]\n",
    "mAP50 = [40.5, 40.5, 40.4, 40.6, 40.3]\n",
    "mAP75 = [26.6, 26.8, 26.6, 26.7, 26.7]\n",
    "mAP_small = [13.1, 13.8, 12.7, 13.7, 13.4]\n",
    "mAP_medium = [25.2, 24.9, 24.9, 24.9, 25.0]\n",
    "mAP_large = [35.4, 36.1, 36.1, 35.8, 36.1]\n",
    "AR1 = [21.9, 21.8, 22.0, 21.7, 21.8]\n",
    "AR10 = [40.1, 40.3, 40.1, 40.0, 40.0]\n",
    "AR100 = [41.7, 42.0, 41.7, 41.7, 41.7]\n",
    "AR_small = [23.3, 24.3, 23.9, 24.1, 24.0]\n",
    "AR_medium = [44.4, 44.4, 44.2, 44.2, 44.1]\n",
    "AR_large = [58.8, 59.4, 59.2, 59.0, 59.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [22.0, 22.0, 22.1, 22.1, 21.9]\n",
    "mAP50 = [37.9, 37.6, 38.0, 38.0, 37.7]\n",
    "mAP75 = [22.8, 22.8, 23.0, 23.0, 22.7]\n",
    "mAP_small = [11.0, 11.9, 10.7, 11.8, 11.5]\n",
    "mAP_medium = [22.3, 22.1, 22.3, 22.1, 22.3]\n",
    "mAP_large = [32.1, 32.4, 32.6, 32.4, 32.3]\n",
    "AR1 = [19.7, 19.6, 19.9, 19.6, 19.7]\n",
    "AR10 = [35.7, 35.8, 35.8, 35.8, 35.7]\n",
    "AR100 = [37.2, 37.2, 37.2, 37.2, 37.2]\n",
    "AR_small = [20.1, 21.3, 21.1, 21.0, 21.3]\n",
    "AR_medium = [39.7, 39.5, 39.5, 39.5, 39.5]\n",
    "AR_large = [52.4, 52.3, 52.7, 52.8, 52.6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### S1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [25.7, 25.7, 25.6, 25.6, 25.7]\n",
    "mAP50 = [42.5, 42.4, 42.3, 42.2, 42.4]\n",
    "mAP75 = [27.2, 27.1, 27.1, 26.9, 27.4]\n",
    "mAP_small = [12.6, 12.5, 13.1, 12.5, 12.5]\n",
    "mAP_medium = [25.9, 25.6, 25.5, 25.3, 25.7]\n",
    "mAP_large = [36.0, 36.1, 36.1, 36.3, 36.4]\n",
    "AR1 = [22.1, 22.1, 22.1, 22.1, 22.0]\n",
    "AR10 = [40.6, 40.6, 40.7, 40.5, 40.4]\n",
    "AR100 = [42.5, 42.4, 42.5, 42.3, 42.2]\n",
    "AR_small = [24.3, 24.2, 24.7, 24.2, 24.3]\n",
    "AR_medium = [45.3, 45.3, 45.2, 44.8, 45.0]\n",
    "AR_large = [59.4, 59.2, 59.3, 59.4, 59.4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [22.8, 22.7, 22.6, 22.8, 22.8]\n",
    "mAP50 = [39.8, 39.6, 39.6, 39.7, 39.8]\n",
    "mAP75 = [23.2, 23.1, 22.8, 23.1, 23.3]\n",
    "mAP_small = [10.4, 10.1, 10.8, 10.2, 10.2]\n",
    "mAP_medium = [23.2, 23.0, 22.9, 22.8, 23.1]\n",
    "mAP_large = [32.9, 33.0, 32.9, 33.2, 33.2]\n",
    "AR1 = [20.3, 20.2, 20.2, 20.3, 20.1]\n",
    "AR10 = [36.3, 36.1, 36.1, 36.1, 36.0]\n",
    "AR100 = [37.8, 37.6, 37.6, 37.7, 37.5]\n",
    "AR_small = [20.9, 20.5, 21.0, 20.8, 20.6]\n",
    "AR_medium = [40.8, 40.9, 40.7, 40.6, 40.6]\n",
    "AR_large = [53.4, 53.0, 52.9, 53.3, 53.4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### one-shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [9.4, 9.4, 9.5, 9.4, 9.3]\n",
    "mAP50 = [16.9, 16.7, 16.8, 16.8, 16.6]\n",
    "mAP75 = [9.7, 9.6, 9.7, 9.7, 9.6]\n",
    "mAP_small = [5.4, 5.4, 5.9, 6.2, 5.3]\n",
    "mAP_medium = [9.5, 9.2, 9.1, 9.5, 9.0]\n",
    "mAP_large = [14.4, 14.8, 14.9, 14.2, 14.9]\n",
    "AR1 = [11.2, 11.0, 10.8, 11.0, 10.9]\n",
    "AR10 = [28.0, 28.2, 28.2, 28.0, 28.2]\n",
    "AR100 = [29.4, 29.4, 29.4, 29.2, 29.6]\n",
    "AR_small = [17.9, 17.5, 14.5, 14.6, 14.4]\n",
    "AR_medium = [32.4, 31.7, 32.0, 31.4, 32.0]\n",
    "AR_large = [45.8, 45.7, 45.8, 45.1, 46.6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [7.3, 7.4, 7.5, 7.4, 7.4]\n",
    "mAP50 = [14.7, 14.7, 14.9, 15.0, 14.8]\n",
    "mAP75 = [6.5, 6.7, 7.0, 6.5, 6.8]\n",
    "mAP_small = [4.1, 4.2, 4.4, 4.7, 4.2]\n",
    "mAP_medium = [7.1, 7.1, 7.2, 7.6, 7.0]\n",
    "mAP_large = [11.9, 12.2, 12.4, 11.9, 12.4]\n",
    "AR1 = [9.1, 9.2, 9.0, 9.3, 9.2]\n",
    "AR10 = [23.3, 23.8, 23.8, 23.7, 23.7]\n",
    "AR100 = [24.5, 24.7, 24.8, 24.7, 24.7]\n",
    "AR_small = [15.1, 15.3, 11.5, 11.8, 11.7]\n",
    "AR_medium = [26.8, 26.6, 27.2, 26.5, 26.7]\n",
    "AR_large = [39.3, 38.8, 39.4, 39.2, 39.8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### S2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [24.2, 24.4, 24.3, 24.4, 24.3]\n",
    "mAP50 = [40.3, 40.7, 40.5, 40.6, 40.5]\n",
    "mAP75 = [26.0, 26.2, 26.2, 26.0, 26.1]\n",
    "mAP_small = [12.5, 12.5, 13.1, 13.2, 12.7]\n",
    "mAP_medium = [25.0, 25.4, 25.2, 25.1, 25.0]\n",
    "mAP_large = [35.0, 35.3, 35.1, 35.6, 35.7]\n",
    "AR1 = [21.3, 21.4, 21.4, 21.4, 21.4]\n",
    "AR10 = [39.7, 39.6, 39.8, 39.7, 39.7]\n",
    "AR100 = [41.3, 41.3, 41.5, 41.4, 41.2]\n",
    "AR_small = [24.2, 24.0, 24.3, 24.3, 23.9]\n",
    "AR_medium = [44.2, 44.4, 44.4, 44.1, 44.1]\n",
    "AR_large = [59.7, 59.8, 59.9, 60.1, 59.8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [21.0, 21.1, 21.0, 21.0, 21.1]\n",
    "mAP50 = [37.1, 37.4, 37.3, 37.4, 37.2]\n",
    "mAP75 = [21.6, 21.9, 21.6, 21.4, 21.8]\n",
    "mAP_small = [10.2, 10.2, 10.8, 10.8, 10.3]\n",
    "mAP_medium = [21.6, 21.9, 21.7, 21.7, 21.7]\n",
    "mAP_large = [30.9, 31.2, 31.0, 31.2, 31.7]\n",
    "AR1 = [19.1, 19.2, 19.2, 19.1, 19.3]\n",
    "AR10 = [34.9, 34.9, 35.0, 34.9, 34.9]\n",
    "AR100 = [36.2, 36.3, 36.4, 36.2, 36.2]\n",
    "AR_small = [20.8, 20.6, 20.7, 20.9, 20.5]\n",
    "AR_medium = [38.6, 38.9, 38.8, 38.7, 38.7]\n",
    "AR_large = [52.8, 53.0, 52.6, 53.0, 53.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### one-shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [11.6, 11.8, 11.6, 11.7, 11.7]\n",
    "mAP50 = [20.0, 20.0, 19.9, 19.9, 20.0]\n",
    "mAP75 = [12.1, 12.3, 11.9, 12.3, 11.8]\n",
    "mAP_small = [6.3, 6.1, 6.2, 6.4, 6.6]\n",
    "mAP_medium = [9.5, 9.9, 9.3, 9.9, 9.9]\n",
    "mAP_large = [19.3, 19.9, 18.9, 19.5, 18.9]\n",
    "AR1 = [13.6, 13.3, 13.3, 13.1, 13.3]\n",
    "AR10 = [29.3, 29.1, 29.0, 29.1, 29.2]\n",
    "AR100 = [30.4, 30.1, 30.2, 30.3, 30.4]\n",
    "AR_small = [15.6, 14.6, 14.9, 15.2, 15.4]\n",
    "AR_medium = [30.6, 30.6, 30.7, 30.7, 30.9]\n",
    "AR_large = [48.0, 48.1, 48.2, 48.1, 49.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [10.0, 10.3, 10.2, 10.3, 10.2]\n",
    "mAP50 = [17.7, 18.1, 18.0, 18.2, 18.1]\n",
    "mAP75 = [10.3, 10.5, 10.5, 10.6, 10.5]\n",
    "mAP_small = [5.2, 4.9, 5.1, 5.2, 5.3]\n",
    "mAP_medium = [8.5, 8.6, 8.3, 8.7, 8.7]\n",
    "mAP_large = [16.9, 17.9, 16.8, 17.4, 16.9]\n",
    "AR1 = [12.0, 11.9, 12.0, 11.9, 12.0]\n",
    "AR10 = [26.0, 25.9, 26.0, 25.8, 26.1]\n",
    "AR100 = [27.1, 26.8, 27.0, 27.0, 27.2]\n",
    "AR_small = [12.7, 12.0, 12.5, 12.7, 12.7]\n",
    "AR_medium = [27.8, 27.5, 27.6, 27.7, 27.9]\n",
    "AR_large = [43.9, 44.0, 44.0, 43.9, 44.6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### S3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [25.8, 25.8, 25.8, 25.8, 25.9]\n",
    "mAP50 = [41.4, 41.5, 41.6, 41.5, 41.7]\n",
    "mAP75 = [28.0, 27.9, 27.9, 28.0, 28.0]\n",
    "mAP_small = [12.8, 12.7, 12.7, 12.7, 12.6]\n",
    "mAP_medium = [24.9, 25.2, 25.2, 25.3, 25.2]\n",
    "mAP_large = [38.0, 38.3, 38.2, 38.2, 38.4]\n",
    "AR1 = [22.5, 22.4, 22.3, 22.4, 22.6]\n",
    "AR10 = [41.2, 41.0, 41.1, 40.9, 41.0]\n",
    "AR100 = [42.8, 42.8, 42.8, 42.6, 42.7]\n",
    "AR_small = [23.4, 23.8, 23.4, 23.6, 23.3]\n",
    "AR_medium = [45.1, 45.3, 45.1, 45.0, 45.2]\n",
    "AR_large = [61.4, 61.6, 61.6, 61.5, 61.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [22.3, 22.3, 22.4, 22.4, 22.4]\n",
    "mAP50 = [38.5, 38.7, 38.8, 38.6, 38.9]\n",
    "mAP75 = [22.7, 22.8, 22.6, 23.0, 22.8]\n",
    "mAP_small = [10.4, 10.2, 10.3, 10.2, 10.2]\n",
    "mAP_medium = [21.6, 21.9, 22.0, 22.0, 21.9]\n",
    "mAP_large = [34.0, 34.4, 34.0, 34.3, 34.4]\n",
    "AR1 = [20.2, 20.4, 20.1, 20.2, 20.4]\n",
    "AR10 = [36.5, 36.5, 36.5, 36.4, 36.4]\n",
    "AR100 = [37.8, 37.9, 37.9, 37.8, 37.8]\n",
    "AR_small = [19.8, 19.8, 19.7, 19.9, 19.7]\n",
    "AR_medium = [40.1, 40.2, 40.4, 40.1, 40.2]\n",
    "AR_large = [56.1, 56.3, 56.3, 56.5, 56.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### one-shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [10.0, 9.7, 9.8, 9.8, 9.5]\n",
    "mAP50 = [18.5, 18.2, 18.3, 18.4, 17.5]\n",
    "mAP75 = [10.0, 9.2, 9.4, 9.7, 9.1]\n",
    "mAP_small = [6.5, 6.6, 6.9, 6.9, 6.7]\n",
    "mAP_medium = [9.5, 9.6, 8.8, 9.4, 8.7]\n",
    "mAP_large = [18.1, 17.5, 17.8, 17.2, 17.1]\n",
    "AR1 = [9.7, 9.8, 9.6, 9.5, 9.3]\n",
    "AR10 = [25.0, 25.1, 25.0, 25.0, 24.8]\n",
    "AR100 = [26.0, 26.0, 26.0, 26.1, 25.8]\n",
    "AR_small = [16.0, 16.3, 16.5, 16.4, 16.1]\n",
    "AR_medium = [26.3, 26.6, 26.2, 26.8, 26.1]\n",
    "AR_large = [42.6, 43.3, 42.3, 42.1, 41.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [9.1, 9.0, 9.0, 9.0, 8.7]\n",
    "mAP50 = [17.5, 17.3, 17.5, 17.6, 17.1]\n",
    "mAP75 = [8.7, 8.6, 8.5, 8.5, 8.3]\n",
    "mAP_small = [5.4, 5.4, 5.7, 5.7, 5.6]\n",
    "mAP_medium = [8.5, 8.9, 7.9, 8.3, 7.6]\n",
    "mAP_large = [17.1, 16.4, 16.7, 16.4, 16.6]\n",
    "AR1 = [9.5, 9.6, 9.4, 9.3, 9.0]\n",
    "AR10 = [23.1, 23.2, 23.0, 23.0, 22.8]\n",
    "AR100 = [24.0, 24.0, 23.9, 24.0, 23.6]\n",
    "AR_small = [14.6, 14.4, 14.8, 14.4, 14.4]\n",
    "AR_medium = [24.5, 24.5, 24.4, 24.6, 23.5]\n",
    "AR_large = [38.4, 38.7, 38.4, 38.4, 37.8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### S4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [25.1, 25.0, 25.1, 25.1, 25.0]\n",
    "mAP50 = [40.8, 40.8, 41.1, 41.0, 40.8]\n",
    "mAP75 = [26.8, 26.5, 26.8, 26.9, 26.8]\n",
    "mAP_small = [13.2, 12.4, 12.6, 13.5, 12.7]\n",
    "mAP_medium = [23.8, 23.9, 24.0, 23.8, 23.7]\n",
    "mAP_large = [36.7, 36.2, 36.0, 36.2, 36.5]\n",
    "AR1 = [21.3, 21.6, 21.6, 21.4, 21.5]\n",
    "AR10 = [40.2, 40.3, 40.3, 40.3, 40.2]\n",
    "AR100 = [42.3, 42.2, 42.4, 42.3, 42.3]\n",
    "AR_small = [24.8, 23.9, 25.1, 24.5, 25.1]\n",
    "AR_medium = [44.4, 44.5, 44.7, 44.3, 44.4]\n",
    "AR_large = [59.6, 58.8, 58.9, 59.0, 59.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [22.1, 22.0, 22.1, 22.2, 22.0]\n",
    "mAP50 = [37.9, 37.9, 38.0, 38.2, 37.7]\n",
    "mAP75 = [23.3, 23.1, 23.2, 23.1, 23.1]\n",
    "mAP_small = [10.9, 10.1, 10.3, 11.2, 10.3]\n",
    "mAP_medium = [20.8, 20.9, 20.9, 20.8, 20.8]\n",
    "mAP_large = [33.3, 33.0, 32.7, 32.8, 32.9]\n",
    "AR1 = [19.3, 19.6, 19.7, 19.4, 19.6]\n",
    "AR10 = [35.8, 35.9, 35.9, 35.9, 35.9]\n",
    "AR100 = [37.6, 37.5, 37.7, 37.6, 37.7]\n",
    "AR_small = [21.7, 20.8, 21.6, 21.3, 21.7]\n",
    "AR_medium = [39.3, 39.1, 39.5, 39.3, 39.4]\n",
    "AR_large = [53.8, 53.8, 53.3, 53.4, 53.7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### one-shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [10.5, 10.7, 10.7, 10.5, 10.4]\n",
    "mAP50 = [19.0, 19.2, 19.0, 19.0, 18.8]\n",
    "mAP75 = [10.7, 10.7, 10.8, 10.8, 10.1]\n",
    "mAP_small = [5.6, 5.4, 6.1, 5.9, 5.9]\n",
    "mAP_medium = [10.4, 10.8, 10.6, 10.4, 10.0]\n",
    "mAP_large = [17.0, 16.9, 16.2, 16.4, 16.5]\n",
    "AR1 = [11.6, 12.0, 12.0, 11.7, 11.7]\n",
    "AR10 = [27.7, 27.7, 27.9, 27.8, 27.7]\n",
    "AR100 = [29.5, 29.8, 29.6, 29.5, 29.5]\n",
    "AR_small = [15.0, 14.7, 15.0, 14.7, 14.8]\n",
    "AR_medium = [32.6, 33.5, 33.2, 33.6, 32.8]\n",
    "AR_large = [47.1, 48.2, 47.8, 47.2, 47.3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAP = [8.6, 8.6, 8.6, 8.4, 8.3]\n",
    "mAP50 = [17.0, 16.8, 17.0, 16.7, 16.8]\n",
    "mAP75 = [8.0, 7.9, 7.8, 7.8, 7.5]\n",
    "mAP_small = [4.0, 3.8, 4.2, 4.3, 4.1]\n",
    "mAP_medium = [8.8, 9.0, 9.0, 8.8, 8.5]\n",
    "mAP_large = [14.8, 14.6, 14.1, 14.4, 14.2]\n",
    "AR1 = [10.3, 10.4, 10.6, 10.2, 10.1]\n",
    "AR10 = [24.2, 24.3, 24.3, 24.3, 24.3]\n",
    "AR100 = [25.9, 26.1, 25.9, 25.8, 25.9]\n",
    "AR_small = [12.4, 12.0, 12.4, 12.4, 12.3]\n",
    "AR_medium = [30.2, 31.0, 30.2, 30.6, 30.1]\n",
    "AR_large = [42.4, 42.4, 42.1, 41.5, 41.7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyses main table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# background detection\n",
    "mAP50_s1 = [42.5, 42.4, 42.3, 42.2, 42.4]\n",
    "mAP50_s2 = [40.3, 40.7, 40.5, 40.6, 40.5]\n",
    "mAP50_s3 = [41.4, 41.5, 41.6, 41.5, 41.7]\n",
    "mAP50_s4 = [40.8, 40.8, 41.1, 41.0, 40.8]\n",
    "\n",
    "mAP50 = [mAP50_s1, mAP50_s2, mAP50_s3, mAP50_s4]\n",
    "mAP50_runwise = [np.mean([mAP50_s1[i], mAP50_s2[i], mAP50_s3[i], mAP50_s4[i]]) for i in range(5)]\n",
    "mean_mAP50 = [np.mean(mAP50[i]) for i in range(4)]\n",
    "std_mAP50 = [np.std(mAP50[i]) for i in range(4)]\n",
    "conf95_mAP50 = [std_mAP50[i]*1.96/np.sqrt(5) for i in range(4)]\n",
    "\n",
    "print('${:.1f}\\pm{:.2f}$'.format(np.mean(mAP50_runwise), np.std(mAP50_runwise)*1.96/np.sqrt(5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# background segmentation\n",
    "mAP50_s1 = [39.8, 39.6, 39.6, 39.7, 39.8]\n",
    "mAP50_s2 = [37.1, 37.4, 37.3, 37.4, 37.2]\n",
    "mAP50_s3 = [38.5, 38.7, 38.8, 38.6, 38.9]\n",
    "mAP50_s4 = [37.9, 37.9, 38.0, 38.2, 37.7]\n",
    "\n",
    "mAP50 = [mAP50_s1, mAP50_s2, mAP50_s3, mAP50_s4]\n",
    "mAP50_runwise = [np.mean([mAP50_s1[i], mAP50_s2[i], mAP50_s3[i], mAP50_s4[i]]) for i in range(5)]\n",
    "mean_mAP50 = [np.mean(mAP50[i]) for i in range(4)]\n",
    "std_mAP50 = [np.std(mAP50[i]) for i in range(4)]\n",
    "conf95_mAP50 = [std_mAP50[i]*1.96/np.sqrt(5) for i in range(4)]\n",
    "\n",
    "print('${:.1f}\\pm{:.2f}$'.format(np.mean(mAP50_runwise), np.std(mAP50_runwise)*1.96/np.sqrt(5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-shot detection\n",
    "mAP50_s1 = [16.9, 16.7, 16.8, 16.8, 16.6]\n",
    "mAP50_s2 = [20.0, 20.0, 19.9, 19.9, 20.0]\n",
    "mAP50_s3 = [18.5, 18.2, 18.3, 18.4, 17.5]\n",
    "mAP50_s4 = [19.0, 19.2, 19.0, 19.0, 18.8]\n",
    "\n",
    "mAP50 = [mAP50_s1, mAP50_s2, mAP50_s3, mAP50_s4]\n",
    "mAP50_runwise = [np.mean([mAP50_s1[i], mAP50_s2[i], mAP50_s3[i], mAP50_s4[i]]) for i in range(5)]\n",
    "mean_mAP50 = [np.mean(mAP50[i]) for i in range(4)]\n",
    "std_mAP50 = [np.std(mAP50[i]) for i in range(4)]\n",
    "conf95_mAP50 = [std_mAP50[i]*1.96/np.sqrt(5) for i in range(4)]\n",
    "\n",
    "print('${:.1f}\\pm{:.2f}$'.format(np.mean(mAP50_runwise), np.std(mAP50_runwise)*1.96/np.sqrt(5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-shot segmentation\n",
    "mAP50_s1 = [14.7, 14.7, 14.9, 15.0, 14.8]\n",
    "mAP50_s2 = [17.7, 18.1, 18.0, 18.2, 18.1]\n",
    "mAP50_s3 = [17.5, 17.3, 17.5, 17.6, 17.1]\n",
    "mAP50_s4 = [17.0, 16.8, 17.0, 16.7, 16.8]\n",
    "\n",
    "mAP50 = [mAP50_s1, mAP50_s2, mAP50_s3, mAP50_s4]\n",
    "mAP50_runwise = [np.mean([mAP50_s1[i], mAP50_s2[i], mAP50_s3[i], mAP50_s4[i]]) for i in range(5)]\n",
    "mean_mAP50 = [np.mean(mAP50[i]) for i in range(4)]\n",
    "std_mAP50 = [np.std(mAP50[i]) for i in range(4)]\n",
    "conf95_mAP50 = [std_mAP50[i]*1.96/np.sqrt(5) for i in range(4)]\n",
    "\n",
    "print('${:.1f}\\pm{:.2f}$'.format(np.mean(mAP50_runwise), np.std(mAP50_runwise)*1.96/np.sqrt(5)))"
   ]
  }
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