diff --git a/docs/_config.yml b/docs/_config.yml
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--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -58,7 +58,7 @@ plugins:
 author:
   name   : "David Pascual-Hernández"
   avatar : "/assets/images/profile.jpeg"
-  bio    : ":man_student: PhD Student and Research Associate @ URJC<br>:camera_flash: Computer Vision<br>:robot: AI<br>:blue_car:Autonomous Driving"
+  bio    : ":man_student: PhD Student and Researcher @ URJC<br>:camera_flash: Computer Vision<br>:robot: Artificial Intelligence<br>:blue_car: Autonomous Driving"
   links:
     - label: "LinkedIn"
       icon: "fas fa-fw fa-link"
diff --git a/docs/_posts/2024-11-22-detectionmetrics-v2.md b/docs/_posts/2024-11-22-detectionmetrics-v2.md
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+---
+title: "DetectionMetrics v2"
+categories:
+  - Blog
+tags:
+  - Semantic segmentation
+  - DetectionMetrics
+toc: true
+toc_sticky: true
+---
+
+Our current project requires a tool to efficiently evaluate image and LiDAR segmentation models, one that's compatible with the most common datasets and deep learning frameworks. DetectionMetrics [[1]](#references), developed by RoboticsLabURJC members, serves a similar purpose but focuses on object detection tasks. As stated on the project webpage:
+
+> Detection Metrics is an application that provides a toolbox of utilities oriented to simplify the development and testing of solutions based on object detection. The application comes with a GUI (based on Qt) but it can also be used through command line.
+>
+
+Initially, we considered extending DetectionMetrics to meet these needs. However, modernizing the codebase and adapting it for our purposes would require too much effort. Instead, we decided to build a streamlined version almost from scratch that focuses on our specific requirements.
+
+With that goal in mind, we have built ***DetectionMetrics v2***. It is a library written in Python with a focus on interoperability between different datasets and deep learning frameworks. It is easily extensible and as of now it supports:
+
+- **Datasets**: Rellis3D, GOOSE and GAIA (our custom format)
+- **Deep-learning frameworks**: TensorFlow and PyTorch.
+
+# Structure
+
+The library consists of three main components:
+
+- **Datasets**: Contains a parent `ImageSegmentationDataset` class and child objects for each supported dataset. Internally, datasets store a pandas *DataFrame* that links pairs of label and image filenames with their sample IDs and splits, along with a dictionary containing the dataset's ontology. After reading a dataset, we can store it in our custom GAIA format (the *DataFrame* in a Parquet file plus the image files). The implementation also supports merging datasets that have compatible ontologies or by using an *ontology translation* file.
+- **Models**: Contains a parent `ImageSegmenationModel` class and child objects for each supported dataset. Each model requires three files: the model file itself, an ontology file, and a configuration file that specifies parameters like image size and normalization values for different frameworks. Once loaded, a model can perform single-image inference or evaluate entire datasets (provided the model and dataset ontologies are compatible). Evaluation results are returned as a *DataFrame* that can be exported to various formats (e.g., CSV).
+- **Utils**: Contains miscellaneous utility functions. Notable among these is `utils.metrics`, which defines the metrics used for model evaluation (e.g., IoU).
+
+We also provide examples for loading the compatible datasets and performing evaluation with PyTorch and TensorFlow models in the [examples](https://github.com/JdeRobot/DetectionMetrics/tree/revert-232-revert-231-dph/v2/examples) directory.
+
+# Future work
+
+In the near future, we plan to expand our available metrics, add ONNX support, and incorporate LiDAR datasets. We also aim to develop a GUI for exploring loaded datasets and evaluation results. On the infrastructure side, we will implement testing and set up automated deployment of our library as a PIP package, with automatic documentation updates.
+
+# References
+
+[1] [S. Paniego, V. Sharma, J. Cañas. "Open Source Assessment of Deep Learning Visual Object Detection," in Sensors, vol. 22, no. 12, 2022.](https://www.mdpi.com/1424-8220/22/12/4575)
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