README.md

MSFFN

An MultiSpectral Feature Fusion Network (MSFFN) for object detection or pedestrian detection based on KAIST Multispectral Pedestrian Detection Benchmark.

Download KAIST dataset

Download KAIST Multispectral Pedestrian Detection Benchmark [KAIST]

Extract all of these tars into one directory and rename them, which should have the following basic structure.

Kaist datasets path

1. data/dataset/kaist/annos

2. data/dataset/kaist/images

2.1) data/dataset/kaist/images/lwir

2.2) data/dataset/kaist/images/visible

3. data/dataset/kaist/imgsets

3.1) data/dataset/kaist/imgsets/train.txt

3.2) data/dataset/kaist/imgsets/val.txt

Make kaist train and val annotation

$ python scripts/annotation.py

Then edit your `core/config.py` to make some necessary configurations

__C.YOLO.CLASSES = "data/classes/pedestrian.names"

__C.TRAIN.ANNOT_PATH = "data/dataset/pedestrian_train.txt"

__C.TEST.ANNOT_PATH = "data/dataset/pedestrian_val.txt"

Train KAIST dataset

Two files are required as follows:

• data/classes/pedestrian.names

   person
  

• data/dataset/pedestrian_train.txt

   data/dataset/kaist/images/visible/set03_V001_I00909.jpg data/dataset/kaist/images/lwir/set03_V001_I00909.jpg 323,319,345,273,0 287,215,301,249,0 279,222,288,244,0 1,240,36,441,0
  

• data/dataset/pedestrian_val.txt

   data/dataset/kaist/images/visible/set00_V008_I00627.jpg data/dataset/kaist/images/lwir/set00_V008_I00627.jpg 385,228,408,285,0
  

Train method

$ python train.py
$ tensorboard --logdir data/log/train

Evaluate method

$ python evaluate.py

mAP

$ python evaluate.py
$ cd mAP
$ python main.py

Demo

Two steps:

Step1: freeze graph from ckpt file into pb file in order to speed up

Step2: config pb_file and images or videos file, or num_classes / input_size / score_thresh / iou_thresh in demo.py

$ python scripts/freeze_graph_ckpt2pb.py
$ python demo.py