Awesome
DSSD MXNET
Features
- An MXNet implementation of DSSD : Deconvolutional Single Shot Detector.
- MXNET implementation for TDM architecture,Beyond Skip Connections Top Down Modulation for Object Detection.
- Code implementation in this repository is based on the MXNET implementation of SSD available here.
Specific VOC mAP Results
| backbone | SSD | SSD_min_loss | DSSD* | DSSD_stage1 | DSSD_stage2 | SSD+TDM |
|---|---|---|---|---|---|---|
| vgg16-512 | 75.56 | 75.35 | 75.85 | 65.16 | 75.77 | 76.80 |
| vgg16-300 | 74.65 | 74.59 | 75.74 | —— | —— | —— |
| resnet101-512 | 78.43 | 78.02 | 79.25 | 71.98 | 78.19 | 79.18 |
| resnet101-321 | 75.18 | 74.80 | 75.54 | —— | —— | —— |
- DSSD*: results by use our traning strategy,for more details,please see here
Requirements
We tested our code on:
Ubuntu 16.04, Python 2.7 with
numpy(1.11.0), cv2(3.3.0-dev)
mxnet 0.11.0
Preparation for Training
1.Download the converted pretrained vgg16_reduced model here.
2.Prepare VOC datasets and generate .rec files by using tools/prepare_pascal.sh
3.Set TDM or DSSD mode in function get_config from symbol/symbol_factory.py.By default, DSSD mode is used,please set all configs by your needs.
4.start training
python train.py
or choice a bash file which provide in ./script/ to run some default parameters setting,such like try the two stage training strategy.
bash scripts/stage1_dssd_train_res_voc.sh
Demo
- Download model, available at here, and place it in the model folder.
- run demo.py
References
1.SSD: Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC. SSD: Single shot multibox detector. InEuropean Conference on Computer Vision 2016 Oct 8 (pp. 21-37). Springer International Publishing.Link
2.DSSD: Fu CY, Liu W, Ranga A, Tyagi A, Berg AC. DSSD: Deconvolutional Single Shot Detector. arXiv preprint arXiv:1701.06659. 2017 Jan 23. Link
3.TDM: Shrivastava A, Sukthankar R, Malik J, Gupta A. Beyond Skip Connections: Top-Down Modulation for ObjectDetection. arXiv preprint arXiv:1612.06851. 2016 Dec 20.Link