stefanherdy / jess

Joint Energy-Based Semantic Segmentation

JESS

Joint Energy-Based Semantic Segmentation

Installation

$ git clone https://github.com/stefanherdy/JESS.git

Usage

- Run train_jess.py with "python train_jess.py".
    You can specify the following parameters:
    --batch_size, type=int, default=8, help="Batch Size"
    --learnrate, type=int, default=0.0001, help='learn rate of optimizer'
    --p_x_weight, type=int, default=0.01, help='weight of energy based optimization'
    --optimizer, choices=['sgd', 'adam'], default='adam'
    --eval_every, type=int, default=1, help="Epochs between evaluation"
    --print_every, type=int, default=1, help="Epochs between print"
    --ckpt_every, type=int, default=20, help="Epochs between checkpoint save"
    --energy, choices=['True', 'False'], default='True', help="Set p(x) optimization on(True)/off(False)"
    --num_classes, type=int, default=8, help="Number of classes"
    --num_tests, type=int, default=10, help="Number of tests"
    --test, choices=['norm', 'jess'], default='norm', help="Normal test or Joint Energy-Based Sematic Segmentation"
    --set, choices=['usa', 'john_handy', 'john_cam'], default='usa', help="Dataset"

    Example usage:
    "python train_jess.py --test norm --set usa --learnrate 0.00001 --batch_size 16

    
- To evaluate the model run evaluate_model.py with "python evaluate_model.py".
    You can specify the following parameters:
    --test, choices=['norm', 'jess'], default='norm', help="Normal test or Joint Energy-Based Sematic Segmentation"
    --set, choices=['usa', 'john_handy', 'john_cam'], default='norm', help="Dataset"
    --num_classes, type=int, default=8, help="Number of classes"
    --batch_size, type=int, default=8, help="Batch Size"

    Example usage:
    "python evaluate_model.py --test norm --set usa 

    Make sure you performed the training before, so that the models can be loaded for evaluation.

    
- You can run neighbors.py to run the neighbor analysis with "python neighbors.py".
    You can specify the following parameters:
    --test, choices=['norm', 'jess'], default='norm', help="Normal test or Joint Energy-Based Sematic Segmentation"
    --set, choices=['usa', 'john_handy', 'john_cam'], default='norm', help="Dataset"

    Example usage:
    "python neighbors.py --test norm --set usa 

©️ 2023 Stefan Herdy