RGBT-detection/demo/FLIR

• Usage

Usage

We provide the training, testing, and visualization code of thermal-only, early-fusion, middle-fusion and Bayesian fusion. Please change the setting for different fusion methods in the code.

Training:

python demo_train_FLIR.py

Test mAP:

python demo_mAP_FLIR.py

Visualize predicted boxes:

python demo_draw_FLIR.py    

Probabilistic Ensembling:

First, you should save predictions from different models using demo_FLIR_save_predictions.py
# Example thermal only
-> python demo/FLIR/demo_FLIR_save_predictions.py --dataset_path /home/jamie/Desktop/Datasets/FLIR/val --fusion_method thermal_only --model_path trained_models/FLIR/models/thermal_only/out_model_thermal_only.pth

# Example early fusion
-> python demo/FLIR/demo_FLIR_save_predictions.py --dataset_path /home/jamie/Desktop/Datasets/FLIR/val --fusion_method early_fusion --model_path trained_models/FLIR/models/early_fusion/out_model_early_fusion.pth

# Example middle fusion
-> python demo/FLIR/demo_FLIR_save_predictions.py --dataset_path /home/jamie/Desktop/Datasets/FLIR/val --fusion_method middle_fusion --model_path trained_models/FLIR/models/middle_fusion/out_model_middle_fusion.pth

Then, you can change and load the predictions in demo_probEn.py
-> python demo/FLIR/demo_probEn.py --dataset_path /home/jamie/Desktop/Datasets/FLIR/val --prediction_path out/  --score_fusion max --box_fusion argmax

For more example usage, please check run.sh file.