[ADD] Complete the README

.gitignore

@@ -0,0 +1,6 @@
+.venv/
+__pycache__/
+data/
+detectron2/*
+output/
+tiles/

README.md

@@ -2,12 +2,67 @@
 
 This repository contained code for training and using a model for detecting reindeer on satellite images. The repository is currently a **WORK IN PROGRESS**.
 
-## How to use it?
+## Installation
 
-1. Install the dependencies:
+In this repository we are using [detectron2](https://github.com/facebookresearch/detectron2). To install it follow the procedure:
+
+- Create a virtual environment:
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+- Install detectron2 and the other dependancies:
+
+```
+# FIX for installing detectron2, see: https://github.com/Dao-AILab/flash-attention/issues/253
+FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install flash-attn --no-build-isolation 
+
+# Install dependancies
+git clone https://github.com/facebookresearch/detectron2.git
+pip install -e detectron2
+pip install -r requirements.txt
+```
+
+
+- Activate the virtual environment
 
 ```bash
-pip install poetry
-poetry install
+source .venv/bin/activate
 ```
 
+## Import from LabelStudio
+
+We are using [LabelStudio] to annotate the images. After annonating the images in LabelStudio you can **export** annotations and images as a `.zip` file and extract it in a folder `./data/annotations`.
+
+Then, follow the pipeline!
+
+## Create the training dataset dataset
+
+```bash
+python3 dataset.py
+```
+This will create a `tiles` folder containing the tiles of the processed satellite images. The path to the processed satellite images that are to be processed should be updated in the `config.yaml` file.
+
+## Train detectron2
+
+```bash
+python3 train.py
+```
+
+The script should create a folder `output` that contains `model_final.pth`, the logs and other files that `Detectron2` creates.
+
+## Predict
+
+```bash
+python3 predict.py
+```
+
+The script will load the model that has been trained in the previous step to make the predictions. The script will create a folder `./predict/image` containing the predicted bounding boxes and `./predict/json` containing the a `.json` file per image documenting all the bounding box coordinates.
+
+In the `config.yaml` file you can choose to plot the predictions (parameter `TRUE / FALSE`). If `TRUE` plots of the satellite pictures with annotations should be created in `predict/image/`.
+
+![Model prediction for a satellite picture](./predict/image/pred_crop.png)
+
+

config.yaml

@@ -4,6 +4,8 @@
 
 ANNOTATION_PATH: "./data/annotations/result.json"
 IMAGES_FOLDER_PATH: "./data/annotations/"
+
+TILE_ANNOTATION_PATH: "./tiles/new_annotations.json"
 TILE_FOLDER_PATH: "./tiles"
 
 ###################
@@ -12,4 +14,13 @@ TILE_FOLDER_PATH: "./tiles"
 
 TILE_SIZE: 1024
 OVERLAP: 100
-PLOT_ANNOTATION: True
+PLOT_ANNOTATION: False
+
+###############
+# PREDICTIONS #
+###############
+
+CONFIG_FILE: "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
+MODEL_WEIGHTS: "./output/model_final.pth"  # Path to the trained model weights
+OUTPUT_FOLDER: "./predict/"
+PLOT_PREDICTION: True