This is an implementation of EfficientDet for object detection on Keras and Tensorflow. The project is based on the official implementation google/automl, fizyr/keras-retinanet and the qubvel/efficientnet.
Thanks for their hard work. This project is released under the Apache License. Please take their licenses into consideration too when use this project.
Updates
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.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.PASCAL VOC
python3 eval/common.py
to evaluate pascal model by specifying model path there.phi | 0 | 1 |
---|---|---|
w/o weighted | 0.8029 | |
w/ weighted | 0.7892 |
MSCOCO
python3 eval/coco.py
to evaluate coco model by specifying model path there.phi | mAP |
---|---|
0 | 0.334 weights, results |
1 | 0.393 weights, results |
2 | 0.424 weights, results |
3 | 0.454 weights, results |
4 | 0.483 weights, results |
python3 inference.py
to test your image by specifying image path and model path there.