Add reference to COCO evaluation metrics

classification/evaluation/evaluation-end2end.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b1e57c8a",
+   "id": "8afbd5e3",
    "metadata": {},
    "source": [
     "# Table of contents\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12921db4",
+   "id": "a6143564",
    "metadata": {},
    "source": [
     "## Introduction <a name=\"introduction\"></a>\n",
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d46bd91d",
+   "id": "bafcbf96",
    "metadata": {},
    "source": [
     "## Aggregate Model Evaluation <a name=\"modelevaluation\"></a>\n",
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bd95ca9",
+   "id": "073ce554",
    "metadata": {},
    "source": [
     "If the dataset already exists because it had been saved under the same name before, load the dataset from fiftyone's folder."
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d9c393d6",
+   "id": "8681fc92",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dd071ae",
+   "id": "ab97bece",
    "metadata": {},
    "source": [
     "### Perform detections <a name=\"modeldetect\"></a>\n",
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9294bf2",
+   "id": "39ce167e",
    "metadata": {},
    "source": [
     "### Save detections <a name=\"modeldetectionssave\"></a>\n",
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f75ef7aa",
+   "id": "0c9f9304",
    "metadata": {},
    "source": [
     "### Evaluate detections against ground truth <a name=\"modeldetectionseval\"></a>\n",
@@ -226,12 +226,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1e5e4b9",
+   "id": "9e403f93",
    "metadata": {},
    "source": [
     "### Calculate results and plot them <a name=\"modelshowresults\"></a>\n",
     "\n",
-    "Now we have the performance of the model saved in the `results` variable and can extract various metrics from that. Here we print a simple report of all classes and their precision and recall values as well as the mAP with the metric employed by COCO. Next, a confusion matrix is plotted for each class (in our case only one). Finally, we can show the precision vs. recall curve for a specified threshold value."
+    "Now we have the performance of the model saved in the `results` variable and can extract various metrics from that. Here we print a simple report of all classes and their precision and recall values as well as the mAP with the metric employed by [COCO](https://cocodataset.org/#detection-eval). Next, a confusion matrix is plotted for each class (in our case only one). Finally, we can show the precision vs. recall curve for a specified threshold value."
    ]
   },
   {
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9997cd3f",
+   "id": "2d48bb3f",
    "metadata": {},
    "source": [
     "### View dataset in fiftyone <a name=\"modelfiftyonesession\"></a>\n",
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e89754",
+   "id": "22561d30",
    "metadata": {},
    "source": [
     "## YOLO Model Evaluation <a name=\"yoloevaluation\"></a>\n",
@@ -584,7 +584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5782d392",
+   "id": "6f389582",
    "metadata": {},
    "source": [
     "### Load OIDv6 <a name=\"yololoadoid\"></a>\n",
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bfbf8a4",
+   "id": "1b509862",
    "metadata": {},
    "source": [
     "### Export dataset for conversion <a name=\"yoloexportoid\"></a>\n",
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "065e0dca",
+   "id": "4cbee814",
    "metadata": {},
    "source": [
     "### Merge labels into one <a name=\"yolomergelabels\"></a>\n",
@@ -683,7 +683,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "030e4550",
+   "id": "7edb13a2",
    "metadata": {},
    "source": [
     "### Load YOLOv5 dataset <a name=\"yololoadv5\"></a>\n",
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9ea9ba1",
+   "id": "3ab2c225",
    "metadata": {},
    "source": [
     "In case the yolo dataset already exists because it had been saved earlier, we can simply load the dataset from fiftyone's database."
@@ -731,7 +731,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "42b72a2d",
+   "id": "0b86639e",
    "metadata": {},
    "outputs": [
     {
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ebffbda",
+   "id": "9eb7bb84",
    "metadata": {},
    "source": [
     "### Perform detections <a name=\"yoloperformdetections\"></a>\n",
@@ -801,7 +801,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0789cc2",
+   "id": "24df56d9",
    "metadata": {},
    "source": [
     "### Evaluate detections against ground truth <a name=\"yolodetectionseval\"></a>\n",
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "b6b35ed4",
+   "id": "4aaa4577",
    "metadata": {},
    "outputs": [
     {
@@ -832,12 +832,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "124d92a4",
+   "id": "b0df052d",
    "metadata": {},
    "source": [
     "### Calculate results and plot them <a name=\"yoloshowresults\"></a>\n",
     "\n",
-    "Now we have the performance of the model saved in the `results` variable and can extract various metrics from that. Here we print a simple report of all classes and their precision and recall values as well as the mAP with the metric employed by COCO. Next, a confusion matrix is plotted for each class (in our case only one). Finally, we can show the precision vs. recall curve for a specified threshold value."
+    "Now we have the performance of the model saved in the `results` variable and can extract various metrics from that. Here we print a simple report of all classes and their precision and recall values as well as the mAP with the metric employed by [COCO](https://cocodataset.org/#detection-eval). Next, a confusion matrix is plotted for each class (in our case only one). Finally, we can show the precision vs. recall curve for a specified threshold value."
    ]
   },
   {
@@ -1064,7 +1064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfff898d",
+   "id": "def95455",
    "metadata": {},
    "source": [
     "### View dataset in fiftyone <a name=\"yolofiftyonesession\"></a>\n",