# SAPD
**Repository Path**: RomanticWithoutStatus/SAPD
## Basic Information
- **Project Name**: SAPD
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-13
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# SAPD (Soft Anchor Point Object Detection)
This is an implementation of [SAPD](https://arxiv.org/abs/1911.12448) for object detection on Keras and Tensorflow.
The project is based on [fizyr/keras-retinanet](https://github.com/fizyr/keras-retinanet), [qubvel/efficientnet](https://github.com/qubvel/efficientnet),
[xuannianz/EfficientDet](https://github.com/xuannianz/EfficientDet) and [xuannianz/FSAF](https://github.com/xuannianz/FSAF).
The pretrained EfficientNet weights files are downloaded from [Callidior/keras-applications/releases](https://github.com/Callidior/keras-applications/releases)
Thanks for their hard work.
This project is released under the Apache License. Please take their licenses into consideration too when use this project.
## Train
### build dataset
1. Pascal VOC
* Download VOC2007 and VOC2012, copy all image files from VOC2007 to VOC2012.
* Append VOC2007 train.txt to VOC2012 trainval.txt.
* Overwrite VOC2012 val.txt by VOC2007 val.txt.
2. MSCOCO 2017
* Download images and annotations of coco 2017
* Copy all images into datasets/coco/images, all annotations into datasets/coco/annotations
3. Other types please refer to [fizyr/keras-retinanet](https://github.com/fizyr/keras-retinanet))
### train
* STEP1: `python3 train.py --snapshot imagenet --phi {0, 1, 2, 3, 4, 5, 6} --gpu 0 --random-transform --compute-val-loss --freeze-backbone --batch-size 32 --steps 1000 pascal|coco datasets/VOC2012|datasets/coco` to start training. The init lr is 1e-3.
* STEP2: `python3 train.py --snapshot xxx.h5 --phi {0, 1, 2, 3, 4, 5, 6} --gpu 0 --random-transform --compute-val-loss --freeze-bn --batch-size 4 --steps 10000 pascal|coco datasets/VOC2012|datasets/coco` to start training when val mAP can not increase during STEP1. The init lr is 1e-4 and decays to 1e-5 when val mAP keeps dropping down.
## Evaluate
1. PASCAL VOC
* `python3 eval/common.py` to evaluate pascal model by specifying model path there.
* The best evaluation results (score_threshold=0.01, mAP50) on VOC2007 test are:
| phi | 0 |
| ---- | ---- |
| mAP50 | [0.7896](https://drive.google.com/open?id=1Ga3NC327LyUeulifzIihUdTiwpZXAKge) |
| weights size | 17M |
2. MSCOCO
* `python3 eval/coco.py` to evaluate coco model by specifying model path there.
## Test
`python3 inference.py` to test your image by specifying image path and model path there.
