classification was missing

Signed-off-by: João Lucas de Sousa Almeida <[email protected]>

docs/index.md

@@ -4,16 +4,16 @@
 
 The purpose of this library is twofold:
 
-1. To integrate prithvi backbones into the TorchGeo framework
-2. To provide generic LightningDataModules that can be built at runtime
+1. To integrate prithvi backbones into the TorchGeo framework.
+2. To provide generic LightningDataModules that can be built at runtime.
 3. To build a flexible fine-tuning framework based on TorchGeo which can be interacted with at different abstraction levels.
 
 This library provides:
 
-- All the functionality in TorchGeo
-- Easy access to prithvi, timm and smp backbones
-- Flexible trainers for Image Segmentation and Pixel Wise Regression (more in progress)
-- Launching of fine-tuning tasks through powerful configuration files
+- All the functionality in TorchGeo.
+- Easy access to prithvi, timm and smp backbones.
+- Flexible trainers for Image Segmentation, Pixel Wise Regression and Classification (more in progress).
+- Launching of fine-tuning tasks through powerful configuration files.
 
 A good starting place is familiarization with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), which this project is built on, and to a certain extent [TorchGeo](https://torchgeo.readthedocs.io/en/stable/)