Remove installation and reproducibility sections from README

Removed sections on reproducibility, installation, and data availability from the README.

Author: angomezu

README.md

@@ -262,112 +262,7 @@ All directories related to raw data, predictions, and model checkpoints are **in
 
 ---
 
-## Reproducibility & Installation
 
-### Continuous Integration and Code Quality
-
-This repository enforces consistent coding standards and documentation to support long-term reproducibility and collaborative research.
-
-All Python code is automatically checked using:
-
-- **Ruff** for PEP8 and PEP257 compliance
-- **Pre-commit hooks** to prevent non-compliant code from being committed locally
-- **GitHub Actions CI** to validate code quality on every push and pull request
-
-The CI pipeline runs the following checks:
-
-```bash
-ruff check .
-ruff format --check .
-```
-Pull requests to the main branch are blocked unless all checks pass, ensuring that the repository remains clean, readable, and reproducible over time.
-
-### Data Availability
-
-Due to data access restrictions associated with Oak Ridge National Laboratory (ORNL), the original datasets used in this study are **not publicly available**. Full reproduction of the reported experimental results therefore requires **authorized access** to the Advanced Plant Phenotyping Laboratory (APPL) data.
-
-That said, the codebase is **dataset-agnostic by design**. Any 3D LiDAR point cloud dataset can be used **provided that**:
-- Point clouds are available in **XYZ format** (e.g., `.txt`, `.pcd`, `.ply`)
-- Point-wise semantic labels are provided (or generated) following a compatible annotation scheme
-- The data can be adapted to the expected input format used by the dataset loader
-
-This enables reuse of the pipeline for **methodological experimentation**, architectural benchmarking, and extension to alternative 3D segmentation tasks.
-
-
-### Installation
-
-The main dependencies of the project are listed below.
-
-**Core Requirements**
-- Python ≥ 3.8
-- CUDA ≥ 11.x (optional, but recommended for training)
-- PyTorch + PyTorch Geometric
-- Open3D
-
-
-### Step 1: Clone the Repository and Create Environment
-
-```bash
-git clone https://github.com/angomezu/geometric-deep-learning-plant-organ-segmentation.git
-cd geometric-deep-learning-plant-organ-segmentation
-
-conda create -n plantseg python=3.9 pip
-conda activate plantseg
-```
-
-### Step 2: Install PyTorch
-
-Install PyTorch with CUDA support (adjust CUDA version if needed):
-
-```bash
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-```
-
-For CPU-only usage:
-
-```bash
-pip install torch torchvision torchaudio
-```
-
-### Step 3: Install PyTorch Geometric
-
-Install PyTorch Geometric and its dependencies:
-
-```bash
-pip install torch-geometric
-```
-
-If you encounter issues, refer to the official installation guide:
-https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html
-
-### Step 4: Install 3D Processing and ML Dependencies
-
-```bash
-pip install open3d numpy scikit-learn tqdm
-```
-
-
-## Notes on Usage
-
-- Training scripts assume point-wise labeled data
-- Data loaders and feature computation logic are implemented in src/dataset.py
-- Visualization utilities require a functioning OpenGL context (for on-screen rendering)
-
-### Users intending to apply the pipeline to new datasets may need to:
-
-- Adapt the annotation format
-- Update normalization statistics
-- Adjust neighborhood radius and voxelization parameters
-
----
-
-### Model checkpoints (.pth)
-
-This project uses PyTorch checkpoint files (`.pth`) to store trained model weights.
-Running `python train.py` will save a checkpoint to `models/` (see the filename in `train.py`).
-Update `MODEL_PATH` (evaluation) and `CHECKPOINT` (visualization) to point to your `.pth`.
-
----
 
 ### Future Research Directions