1. Open an Anaconda prompt.

2. Navigate to the desired directory (e.g., desktop or any other location):

   cd Desktop

3. Clone the repository from GitHub:

   git clone https://github.com/mspci/pdl
   cd pdl

4. Create a conda environment named "maskrcnn" with Python version 3.7.11:

   conda create -n maskrcnn python=3.7.11 -y

5. Activate the newly created conda environment:

   conda activate maskrcnn

6. Install the required Python packages listed in requirements.txt:

   pip install -r requirements.txt

7. Install protobuf using either conda or pip:

   pip install protobuf flask

8. Optional for GPU Acceleration: Install CUDA Toolkit from NVIDIA for GPU acceleration:

   Download and install the CUDA Toolkit appropriate for your system from NVIDIA's CUDA Downloads.

Download the pre-trained weights from the following links and place them in the pdl directory:

mask_rcnn_coco.h5 mrcnn_trained_20e_sgd_sans_aug.h5

To perform training, navigate to the repository directory 'pdl' in the terminal and run the following command:

python remote_sensing_training.py