Merge pull request #100 from FocoosAI/98-docs-fix-missing-links-in-documentation

Refactor model documentation and navigation structure

Author: fcdl94

README.md

@@ -1,15 +1,17 @@
 ![Tests](https://github.com/FocoosAI/focoos/actions/workflows/test.yml/badge.svg??event=push&branch=main)
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FocoosAI/focoos/blob/main/tutorials/training.ipynb)
+[![Documentation](https://img.shields.io/badge/docs-latest-blue)](https://focoosai.github.io/focoos/)
+
 # Welcome to Focoos AI
 
 Focoos AI provides an advanced development platform designed to empower developers and businesses with efficient, customizable computer vision solutions. Whether you're working with data from cloud infrastructures or deploying on edge devices, Focoos AI enables you to select, fine-tune, and deploy state-of-the-art models optimized for your unique needs.
 
-## SDK Overview
+## Overview
 
-<!-- Unlock the full potential of Focoos AI with the Focoos Python SDK! 🚀  -->
-The Focoos Python SDK is your gateway to easily access cutting-edge computer vision models and development tools. With just a few lines of code, you can **fine tune** pre-trained models tailored to your specific needs.
+<!-- Unlock the full potential of Focoos AI with the Focoos library! 🚀  -->
+The Focoos library is your gateway to easily access cutting-edge computer vision models and development tools. With just a few lines of code, you can **fine tune** pre-trained models tailored to your specific needs.
 
-Whether you're working in the cloud or on edge devices, the Focoos Python SDK seamlessly integrates into your workflow, accelerating development and simplifying the implementation of computer vision solutions.
+Whether you're working in the cloud or on edge devices, the Focoos library seamlessly integrates into your workflow, accelerating development and simplifying the implementation of computer vision solutions.
 
 ### Key Features 🔑
 
@@ -27,7 +29,7 @@ Whether you're working in the cloud or on edge devices, the Focoos Python SDK se
 Ready to dive in? Get started with the setup in just a few simple steps!
 
 ## Installation
-**Install** the Focoos Python SDK (for more options, see [setup](https://focoosai.github.io/focoos/setup))
+**Install** the Focoos library (for more options, see [setup](https://focoosai.github.io/focoos/setup))
 
 ```bash linenums="0"
 uv pip install 'focoos @ git+https://github.com/FocoosAI/focoos.git'
@@ -105,7 +107,7 @@ Using Focoos AI helps you save both time and money while delivering high-perform
 - **4x Cheaper** 💰: Our models require up to 4x less computational power, letting you save on hardware or cloud bill while ensuring high-quality results.
 - **Tons of CO2 saved annually per model** 🌱: Our models are energy-efficient, helping you reduce your carbon footprint by using less powerful hardware with respect to mainstream models.
 
-See the list of our models in the [models](https://focoosai.github.io/focoos/models/) section.
+See the list of our models in the [models](https://focoosai.github.io/focoos/models/models) section.
 
 ---
 ### Start now!

docs/concepts.md

@@ -15,7 +15,7 @@ The `FocoosModel` class is the main interface for working with computer vision m
 
 ### Loading Strategies
 
-The primary method for loading models is using the `ModelManager.get()` (see [`ModelManager`](../api/model_manager/#focoos.model_manager.ModelManager)). It supports multiple loading strategies based on the input parameters. The return value is a [Focoos Model](#focoosmodel).
+The primary method for loading models is using the `ModelManager.get()` (see [`ModelManager`](/focoos/api/model_manager/#focoos.model_manager.ModelManager)). It supports multiple loading strategies based on the input parameters. The return value is a [Focoos Model](#focoosmodel).
 
 The ModelManager employs different loading strategies based on the input:
 
@@ -102,7 +102,7 @@ model = ModelManager.get(
 
 #### 4. From ModelInfo Object
 
-The [`ModelInfo`](../api/ports/#focoos.ports.ModelInfo) class represents comprehensive model metadata including architecture specifications, training configuration, class definitions, and performance metrics. This method provides the most programmatic control over model instantiation.
+The [`ModelInfo`](/focoos/api/ports/#focoos.ports.ModelInfo) class represents comprehensive model metadata including architecture specifications, training configuration, class definitions, and performance metrics. This method provides the most programmatic control over model instantiation.
 
 **When to use**: Programmatically construct models, work with dynamic configurations, integrate with custom model management systems, or when you need fine-grained control over model instantiation.
 
@@ -142,7 +142,7 @@ Performs end-to-end inference on input images with automatic preprocessing and p
 - NumPy arrays (`numpy.ndarray`)
 - PyTorch tensors (`torch.Tensor`)
 
-The input images are automatically preprocessed to the correct size and format required by the model. After inference, the raw model outputs are postprocessed into a standardized [`FocoosDetections`](../api/ports/#focoos.ports.FocoosDetections) format that provides easy access to:
+The input images are automatically preprocessed to the correct size and format required by the model. After inference, the raw model outputs are postprocessed into a standardized [`FocoosDetections`](/focoos/api/ports/#focoos.ports.FocoosDetections) format that provides easy access to:
 
 - Detected object classes and confidence scores
 - Bounding box coordinates
@@ -155,7 +155,7 @@ This provides a simple, unified interface for running inference regardless of th
 - `inputs`: Input images in various supported formats (`PIL.Image.Image`, `numpy.ndarray`, `torch.Tensor`)
 - `**kwargs`: Additional arguments passed to postprocessing
 
-**Returns:** [`FocoosDetections`](../api/ports/#focoos.ports.FocoosDetections) containing detection/segmentation results
+**Returns:** [`FocoosDetections`](/focoos/api/ports/#focoos.ports.FocoosDetections) containing detection/segmentation results
 
 **Example:**
 ```python
@@ -176,7 +176,7 @@ for detection in detections.detections:
 ### Training
 Trains the model on provided datasets. The training function accepts:
 
-- `args`: Training configuration ([TrainerArgs](../api/ports/#focoos.ports.TrainerArgs)) specifying the main hyperparameters, among which:
+- `args`: Training configuration ([TrainerArgs](/focoos/api/ports/#focoos.ports.TrainerArgs)) specifying the main hyperparameters, among which:
   - `run_name`: Name for the training run
   - `output_dir`: Name for the output folder
   - `num_gpus`: Number of GPUs to use (must be >= 1)
@@ -186,7 +186,7 @@ Trains the model on provided datasets. The training function accepts:
 - `data_val`: Validation dataset (MapDataset)
 - `hub`: Optional FocoosHUB instance for experiment tracking
 
-The data can be obtained using the [AutoDataset](../api/auto_dataset/#focoos.data.auto_dataset.AutoDataset) helper.
+The data can be obtained using the [AutoDataset](/focoos/api/auto_dataset/#focoos.data.auto_dataset.AutoDataset) helper.
 
 After the training is complete, the model will have updated weights and can be used for inference or export. Furthermore, in the `output_dir` can be found the model metadata (`model_info.json`) and the PyTorch weights (`model_final.pth`).
 
@@ -214,7 +214,7 @@ model.train(train_args, train_dataset, val_dataset, hub=hub)
 ### Model Export
 
 Exports the model to different runtime formats for optimized inference. The main function arguments are:
- - `runtime_type`: specify the target runtime and must be one of the supported (see [RuntimeType](../api/ports/#focoos.ports.RuntimeType))
+ - `runtime_type`: specify the target runtime and must be one of the supported (see [RuntimeType](/focoos/api/ports/#focoos.ports.RuntimeType))
  - `out_dir`: the destination folder for the exported model
  - `image_size`: the target image size, as an optional integer
 
@@ -246,7 +246,7 @@ The `InferModel` class represents an optimized model for inference, typically cr
 
 ### Initialization
 
-InferModel instances are typically created through the `export()` method of a [FocoosModel](#focoosmodel), which handles the model optimization and conversion process. This method allows you to specify the target runtime (see the availables in [`Runtimetypes`](focoos/api/ports/#focoos.ports.RuntimeType)) and the output directory for the exported model. The `export()` method returns an `InferModel` instance that is optimized for fast and efficient inference.
+InferModel instances are typically created through the `export()` method of a [FocoosModel](#focoosmodel), which handles the model optimization and conversion process. This method allows you to specify the target runtime (see the availables in [`Runtimetypes`](/focoos/api/ports/#focoos.ports.RuntimeType)) and the output directory for the exported model. The `export()` method returns an `InferModel` instance that is optimized for fast and efficient inference.
 
 **Example:**
 ```python
@@ -281,7 +281,7 @@ This provides a simple, unified interface for running inference regardless of th
 - `inputs`: Input images in various supported formats (`PIL.Image.Image`, `numpy.ndarray`, `torch.Tensor`)
 - `**kwargs`: Additional arguments passed to postprocessing
 
-**Returns:** [`FocoosDetections`](../api/ports/#focoos.ports.FocoosDetections) containing detection/segmentation results
+**Returns:** [`FocoosDetections`](/focoos/api/ports/#focoos.ports.FocoosDetections) containing detection/segmentation results
 
 **Example:**
 ```python

docs/hub/overview.md

@@ -1,6 +1,8 @@
 # 🚀 Focoos HUB Overview
 The Focoos HUB is a cloud-based platform that provides seamless integration between your local development environment and the Focoos AI ecosystem. It enables you to manage models, datasets, perform remote inference operations, and monitor training progress through a unified API.
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FocoosAI/focoos/blob/main/tutorials/hub.ipynb)
+
 ## What is Focoos HUB?
 
 Focoos HUB serves as your gateway to:

docs/inference.md

@@ -48,7 +48,7 @@ In this section, you'll run a model on the Focoos' servers instead of on your ma
 
 ```python
 model_ref = "<YOUR-MODEL-REF>"
-dataset = hub.get_remote_model(model_ref)
+model = hub.get_remote_model(model_ref)
 ```
 
 Using the model is as simple as it could! Just call it with an image.
@@ -139,7 +139,7 @@ In the following cells, we will export the previous model for one of these and r
 
 ### Torchscript
 
-We already provide multiple inference runtime, that you can see on the [`RuntimeTypes`](focoos/api/ports/#focoos.ports.RuntimeType) enum. Let's select Torchscript as an example.
+We already provide multiple inference runtime, that you can see on the [`RuntimeTypes`](/focoos/api/ports/#focoos.ports.RuntimeType) enum. Let's select Torchscript as an example.
 
 ```python
 from focoos.ports import RuntimeType
@@ -169,7 +169,7 @@ But, let's see its latency, that should be substantially lower than the pure pyt
 optimized_model.benchmark(iterations=10, size=512)
 ```
 
-You can use different runtimes that may fit better your device, such as TensorRT. See the list of available Runtimes at [`RuntimeTypes`](focoos/api/ports/#focoos.ports.RuntimeType). Please note that you need to install the relative packages for onnx and tensorRT for using them.
+You can use different runtimes that may fit better your device, such as TensorRT. See the list of available Runtimes at [`RuntimeTypes`](/focoos/api/ports/#focoos.ports.RuntimeType). Please note that you need to install the relative packages for onnx and tensorRT for using them.
 
 ### ONNX with TensorRT
 ```python