EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
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.