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
-
data/dataset/kaist/annos
-
data/dataset/kaist/images
2.1) data/dataset/kaist/images/lwir
2.2) data/dataset/kaist/images/visible
- 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