feat: updated readme

README.md

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+# Instance Segmentation Metrics
+[![Build and publish](https://github.com/prime-slam/instanseg-metrics/actions/workflows/ci.yml/badge.svg)](https://github.com/prime-slam/instanseg-metrics/actions/workflows/ci.yml)
+
+<p style="font-size: 14pt;">
+     Instanseg is an open-source python library that provides various metrics for evaluating the results of the algorithms for segmenting and associating instances. 
+</p>
+
+<p style="font-size: 14pt;">
+     List of metrics implemented in the library:
+</p>
+
+<ul style="font-size: 14pt;">
+    <li>Summary segmentation metrics <ul style="font-size: 14pt;">
+        <li><a href="https://prime-slam.github.io/evops-metrics/instance_based/panoptic">Panoptic</a></li>
+        <li><a href="https://prime-slam.github.io/evops-metrics/full_statistics/full_statistics">Full statistics</a></li>
+    </ul></li>
+    <li>Instance-based segmentation metrics
+        <ul style="font-size: 14pt;">
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/precision">Precision</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/recall">Recall</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/fScore">F-Score</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/usr">Under segmented ratio</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/osr">Over segmented ratio</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/noise">Noise ratio</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/instance_based/missed">Missed ratio</a></li>
+    </ul></li>
+    <li>Point-based segmentation metrics
+        <ul style="font-size: 14pt;">
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/point_based/iou">Intersection over Union (IoU)</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/point_based/dice">Dice</a></li>
+            <li><a href="https://prime-slam.github.io/evops-metrics/#/metrics/point_based/mean">Mean of some metric for matched instances</a></li>
+    </ul></li>
+</ul>
+
+<p style="font-size: 14pt;">
+    For more, please visit the <a href="https://prime-slam.github.io/evops-metrics">Instanseg documentation</a>.
+</p>
+<p style="font-size: 14pt;">
+    You can also find full information about the project on the <a href="https://evops.netlify.app/">Instanseg project website</a>.
+</p>
+
+# Python quick start
+
+<p style="font-size: 14pt;">
+     Library can be installed using the pip package manager:
+</p>
+
+```bash
+$ # Install package
+$ pip install instanseg
+
+$ # Check installed version of package
+$ pip show instanseg
+```
+
+# Example of usage
+
+<p style="font-size: 14pt;">
+    Below is an example of using the precision metric:
+</p>
+
+```bash
+>>> from instanseg.metrics import precision
+>>> pred_labels = np.array([1, 1, 3, 3])
+>>> gt_labels = np.array([2, 2, 0, 3])
+>>> tp_condition = "iou"
+>>> precision(pred_labels, gt_labels, tp_condition)
+0.5
+```
+
+# Citation
+```
+@misc{kornilova2022evops,
+      title={EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data}, 
+      author={Anastasiia Kornilova, Dmitrii Iarosh, Denis Kukushkin, Nikolai Goncharov, Pavel Mokeev, Arthur Saliou, Gonzalo Ferrer},
+      year={2022},
+      eprint={2204.05799},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+# License
+
+<p style="font-size: 14pt;">
+    This project is licensed under the Apache License - see the <a href="https://github.com/prime-slam/instanseg-metrics/blob/main/LICENSE">LICENSE</a> file for details.
+</p>