SSD Improvements
This repository is forked from the project SSD. I improve SSD from the following aspects:
- add Resnet backbone (Resnet50 and Resnet101)
- add attention mechanism (Squeeze-and-Excitation Module , Convolutional Block Attention Module)
- add feature fusion module
Train
Setting Up Datasets
Pascal VOC
For Pascal VOC dataset, make the folder structure like this:
VOC_ROOT
|__ VOC2007
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ VOC2012
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ ...
Where VOC_ROOT default is datasets folder in current project, you can create symlinks to datasets or export VOC_ROOT="/path/to/voc_root".
COCO
For COCO dataset, make the folder structure like this:
COCO_ROOT
|__ annotations
|_ instances_valminusminival2014.json
|_ instances_minival2014.json
|_ instances_train2014.json
|_ instances_val2014.json
|_ ...
|__ train2014
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.jpg
|__ val2014
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.jpg
|__ ...
Where COCO_ROOT default is datasets folder in current project, you can create symlinks to datasets or export COCO_ROOT="/path/to/coco_root".
Train sample 1
Configs:
Backbone: Resnet50
Input size: 300
SE: False
CBAM: False
FUSION: False
Run:
python train.py --config-file configs/resnet50_ssd300_voc0712.yaml
Train sample 2
Configs:
Backbone: Resnet50
Input size: 300
SE: False
CBAM: False
FUSION: True
Run:
python train.py --config-file configs/resnet50_ssd300_voc0712_feature_fusion.yaml
You can do custom training by modifying the .yaml files.