Docs cleanup and analytics (#24)

* Only show analytics when on prod

* Update docs

* Add content tabs. Add analytics.

Author: IgorSusmelj

docs/about-us.md

@@ -23,9 +23,8 @@ At Lightly, we aim to simplify the complexities of active learning and data mana
 
 Stay updated with the latest developments, tips, and tutorials by following us:
 
-- [GitHub](https://github.com/lightly-ai/labelformat)
-- [Twitter](https://twitter.com/lightly_ai)
-- [LinkedIn](https://www.linkedin.com/company/lightly-ai)
+- [GitHub](https://github.com/lightly-ai)
+- [Twitter](https://x.com/LightlyAI)
 
 ---
 

docs/assets/js/analytics.js

@@ -0,0 +1,4 @@
+if (window.location.hostname !== 'labelformat.com') {
+  console.log("Google Analytics disabled on localhost or non-production environments");
+  window['ga-disable-G-K4PH64C9BM'] = true;
+}

docs/features.md

@@ -5,17 +5,20 @@ Labelformat offers a robust set of features tailored to meet the diverse needs o
 ## Key Features
 
 - **Wide Format Support:**
-    - **Object Detection** 
-    - **Instance Segmentation**
+    - **2D Object Detection:** Bounding box annotations for object localization
+    - **Instance Segmentation:** Pixel-level masks for precise object delineation
 
 - **User-Friendly CLI and Python API:**
-    - **CLI:** Simple commands to convert formats with customizable options.
+    - **CLI:** Simple terminal commands to convert formats with customizable options.
     - **Python API:** Integrate label conversion seamlessly into your Python workflows.
 
 - **Performance Optimizations:**
     - **Memory Conscious:** Processes datasets file-by-file to minimize memory usage.
     - **Minimal Dependencies:** Targets Python 3.7 or higher, ensuring broad compatibility.
 
+- **Cross-Platform Support:**
+    - **Windows, Linux, and macOS:** Works seamlessly across all major operating systems.
+
 - **Reliability and Testing:**
     - **Typed Codebase:** Ensures type safety and easier maintenance.
     - **Round-Trip Tests:** Guarantees label consistency across conversions.
@@ -52,4 +55,4 @@ Labelformat addresses the common challenges faced when dealing with diverse labe
 - **Scalability:** Handles large datasets with minimal memory footprint.
 - **Flexibility:** Supports a growing list of formats and tasks, adapting to evolving project needs.
 
-Explore our [Quick Start Guide](quick_start.md) to begin leveraging Labelformat's powerful features today!
+Explore our [Quick Start Guide](quick-start.md) to begin leveraging Labelformat's powerful features today!

docs/index.md

@@ -1,32 +1,67 @@
 
 ![Labelformat Banner](assets/labelformat_banner.png)
 
-# Labelformat - Label Conversion, Simplified
+# Labelformat - Fast Label Conversion for Computer Vision
 
+**Labelformat** is an open-source Python framework for converting between popular computer vision annotation formats like YOLO, COCO, PascalVOC, and KITTI. Save hours on tedious format conversions and ensure consistency in your workflows.
 
-Welcome to **Labelformat**—an open-source tool designed to effortlessly convert between various popular computer vision label formats. Whether you're adapting labels from downloaded datasets or modifying outputs from labeling tools to suit your model's requirements, Labelformat streamlines the process, saving you time and reducing complexity.
 
-## Why Choose Labelformat?
+## Key Features
+- **Wide Format Support**: COCO, YOLO (v5-v11), PascalVOC, KITTI, Labelbox, and more.
+- **Cross-Platform**: Compatible with Python 3.7+ on Windows, macOS, and Linux.
+- **Flexible Usage**: Intuitive CLI and Python API.
+- **Efficient**: Memory-conscious, optimized for large datasets.
+- **Offline First**: Operates locally without data uploads.
+- **Tested for Accuracy**: Round-trip tests for consistent results.
 
-- **Comprehensive Format Support:** Convert seamlessly between formats like COCO, YOLOv5-11, PascalVOC, KITTI, and more.
-- **Ease of Use:** Intuitive CLI and Python API for flexible integration into your workflows.
-- **Efficiency:** Memory-conscious processing ensures optimal performance, even with large datasets.
-- **Reliability:** Thoroughly tested with round-trip tests to maintain label consistency.
+## Get Started Quickly
 
-## Get Started
+1. **Install via pip**:
+    ```bash
+    pip install labelformat
+    ```
+2. **Convert Labels in One Command**:
+    ```bash
+    labelformat convert --task object-detection \
+                        --input-format coco \
+                        --input-file coco-labels/train.json \
+                        --output-format yolov8 \
+                        --output-file yolo-labels/data.yaml
+    ```
 
-- [Installation](installation.md)
-- [Quick Start Guide](quick_start.md)
-- [Explore Features](features.md)
+## Supported Formats
 
-## Join Our Community
+### **2D Object Detection Label Formats**
 
-Labelformat is actively maintained by [Lightly](https://www.lightly.ai), a company dedicated to building efficient active learning pipelines. We welcome contributions and feedback—check out our [Contributing Guide](contributing.md) to get involved!
+| Format       | Read ✔️ | Write ✔️ |
+|--------------|---------|----------|
+| COCO         | ✔️      | ✔️       |
+| KITTI        | ✔️      | ✔️       |
+| Labelbox     | ✔️      | ❌       |
+| Lightly      | ✔️      | ✔️       |
+| PascalVOC    | ✔️      | ✔️       |
+| YOLOv5 - v11 | ✔️      | ✔️       |
 
 ---
 
+### **2D Instance Segmentation Label Formats**
+
+| Format       | Read ✔️ | Write ✔️ |
+|--------------|---------|----------|
+| COCO         | ✔️      | ✔️       |
+| YOLOv8       | ✔️      | ✔️       |
+
+
+## Explore More
+- [Quick Start Guide](quick-start.md)
+- [Detailed Usage Guide](usage.md)
+- [List of all features](features.md)
+---
+
 ## 📦 Quick Links
 
 - [GitHub Repository](https://github.com/lightly-ai/labelformat)
 - [PyPI Package](https://pypi.org/project/labelformat/)
-- [Documentation](https://labelformat.com)
+- [Documentation](https://labelformat.com)
+
+Labelformat is maintained by [Lightly](https://www.lightly.ai).

docs/installation.md

@@ -4,16 +4,32 @@ Installing **Labelformat** is straightforward. Follow the steps below to set up
 
 ## Prerequisites
 
-- **Python 3.7 or higher:** Ensure you have Python installed. You can download it from the [official website](https://www.python.org/downloads/).
+- **Python 3.7 or higher:** Ensure you have Python installed on Windows, Linux, or macOS.
 - **pip:** Python's package installer. It typically comes with Python installations.
 
-## Installation via PyPI
+## Installation using package managers
 
-You can install Labelformat directly from PyPI using pip:
+Labelformat is available on PyPI and can be installed using various package managers:
 
-```shell
-pip install labelformat
-```
+=== "pip"
+    ```bash
+    pip install labelformat
+    ```
+
+=== "Poetry"
+    ```bash
+    poetry add labelformat
+    ```
+
+=== "Conda"
+    ```bash
+    conda install -c conda-forge labelformat
+    ```
+
+=== "Rye"
+    ```bash
+    rye add labelformat
+    ```
 
 ## Installation from Source
 

docs/tutorials/converting-coco-to-yolov8.md

@@ -4,7 +4,7 @@ This tutorial walks you through converting object detection labels from the COCO
 
 ## Prerequisites
 
-- **Labelformat Installed:** Follow the [Installation Guide](installation.md).
+- **Labelformat Installed:** Follow the [Installation Guide](../installation.md).
 - **COCO Dataset:** Ensure you have a COCO-formatted dataset ready for conversion.
 
 ## Step 1: Prepare Your Dataset
@@ -145,8 +145,4 @@ You've successfully converted COCO labels to YOLOv8 format using both the CLI an
 
 ## Next Steps
 
-- Explore [Converting YOLOv8 to COCO](converting_yolov8_to_coco.md).
-- Learn how to [Handle Labelbox Exports](handling_labelbox_exports.md).
-- Dive deeper with [Advanced Usage](usage.md).
-
-For any questions or issues, feel free to reach out via our [GitHub Issues](https://github.com/lightly-ai/labelformat/issues).
+- Explore other supported 2d object detection formats in the [Supported Object Detection Formats](../formats/object-detection/index.md) section.

docs/usage.md

@@ -1,22 +1,8 @@
-# usage.md
-
 # Detailed Usage Guide
 
 Labelformat offers both a Command-Line Interface (CLI) and a Python API to cater to different workflows. This guide provides in-depth instructions on how to use both interfaces effectively.
 
-## Table of Contents
-
-- [CLI Usage](#cli-usage)
-  - [Basic Conversion Command](#basic-conversion-command)
-  - [Advanced CLI Options](#advanced-cli-options)
-- [Python API Usage](#python-api-usage)
-  - [Basic Conversion](#basic-conversion)
-  - [Customizing Conversion](#customizing-conversion)
-- [Common Tasks](#common-tasks)
-  - [Handling Category Names](#handling-category-names)
-  - [Managing Image Paths](#managing-image-paths)
-
----
+To get a detailed overview of the supported formats and their specifications, please refer to the [Supported Object Detection Formats](formats/object-detection/index.md) section.
 
 ## CLI Usage
 
@@ -57,7 +43,9 @@ labelformat convert --task object-detection --help
 
 **Specifying Category Names:**
 
-Some formats require explicit category names. Use the `--category-names` argument:
+Some formats require explicit category names. The names must be separated by commas and must be in the same order as the categories in the input file.
+
+Use the `--category-names` argument to specify the category names:
 
 ``` shell
 labelformat convert \
@@ -72,7 +60,7 @@ labelformat convert \
 
 **Handling Missing Images:**
 
-When converting formats that require image files (e.g., YOLO to COCO), ensure your image paths are correctly specified. Use `--images-rel-path` to define the relative path to images:
+When converting formats that require image files (e.g., YOLO to COCO), ensure your image paths are correctly specified. Use `--images-rel-path` to define the relative path from the input folder to the images folder:
 
 ``` shell
 labelformat convert \
@@ -176,4 +164,4 @@ labelformat convert \
 - **Consistent Naming:** Maintain consistent naming conventions for categories and files across different formats.
 - **Leverage Round-Trip Tests:** Use Labelformat's testing capabilities to ensure label consistency when converting back and forth between formats.
 
-For more detailed examples and advanced usage scenarios, explore our [Tutorials](tutorials/converting_coco_to_yolov8.md) section.
+For more detailed examples and advanced usage scenarios, explore our [Tutorials](tutorials/converting-coco-to-yolov8.md) section.

mkdocs.yml

@@ -9,6 +9,7 @@ theme:
     - navigation.tabs
     - navigation.tabs.sticky
     - navigation.indexes
+    - content.tabs.link
   palette:
     scheme: slate
     primary: grey
@@ -51,9 +52,26 @@ plugins:
   - git-revision-date-localized:
       type: date
 
+extra:
+  analytics:
+    provider: google
+    property: G-K4PH64C9BM
+  consent:
+    title: Cookie consent
+    description: >-
+      We use cookies to recognize your repeated visits and preferences, as well
+      as to measure the effectiveness of our documentation and whether users
+      find what they're searching for. With your consent, you're helping us to
+      make our documentation better.
+
+extra_javascript:
+  - assets/js/analytics.js
+
 markdown_extensions:
   - admonition
   - codehilite
   - toc:
       permalink: true
   - pymdownx.superfences
+  - pymdownx.tabbed:
+      alternate_style: true