Awesome
SAPD (Soft Anchor Point Object Detection)
This is an implementation of SAPD for object detection on Keras and Tensorflow. The project is based on fizyr/keras-retinanet, qubvel/efficientnet, xuannianz/EfficientDet and xuannianz/FSAF. The pretrained EfficientNet weights files are downloaded from 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
- 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.
- MSCOCO 2017
- Download images and annotations of coco 2017
- Copy all images into datasets/coco/images, all annotations into datasets/coco/annotations
- Other types please refer to 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
-
PASCAL VOC
python3 eval/common.py
to evaluate pascal model by specifying model path there.- The best evaluation results (score_threshold=0.01, mAP<sub>50</sub>) on VOC2007 test are:
phi 0 mAP<sub>50</sub> 0.7896 weights size 17M -
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.