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https://github.com/xuannianz/efficientdet
EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
https://github.com/xuannianz/efficientdet
detection efficientdet keras-efficientdet oriented-object-detection quadrangle-detection tf-efficientdet
Last synced: 6 days ago
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EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow
- Host: GitHub
- URL: https://github.com/xuannianz/efficientdet
- Owner: xuannianz
- License: apache-2.0
- Created: 2019-11-28T06:35:58.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T21:35:23.000Z (over 1 year ago)
- Last Synced: 2025-01-04T22:04:20.066Z (13 days ago)
- Topics: detection, efficientdet, keras-efficientdet, oriented-object-detection, quadrangle-detection, tf-efficientdet
- Language: Python
- Size: 1.46 MB
- Stars: 1,404
- Watchers: 38
- Forks: 394
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EfficientDet
This is an implementation of [EfficientDet](https://arxiv.org/pdf/1911.09070.pdf) for object detection on Keras and Tensorflow.
The project is based on the official implementation [google/automl](https://github.com/google/automl), [fizyr/keras-retinanet](https://github.com/fizyr/keras-retinanet)
and the [qubvel/efficientnet](https://github.com/qubvel/efficientnet).
## About pretrained weights
* The pretrained EfficientNet weights on imagenet are downloaded from [Callidior/keras-applications/releases](https://github.com/Callidior/keras-applications/releases)
* The pretrained EfficientDet weights on coco are converted from the official release [google/automl](https://github.com/google/automl).
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**
- [03/21/2020] Synchronize with the official implementation. [google/automl](https://github.com/google/automl)
- [03/05/2020] Anchor free version. The accuracy is a little lower, but it's faster and smaller.For details, please refer to [xuannianz/SAPD](https://github.com/xuannianz/SAPD)
- [02/20/2020] Support quadrangle detection. For details, please refer to [README_quad](README_quad.md)
## 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 | 1 |
| ---- | ---- | ---- |
| w/o weighted | | [0.8029](https://drive.google.com/open?id=1-QkMq56w4dZOTQUnbitF53NKEiNF9F_Q) |
| w/ weighted | [0.7892](https://drive.google.com/open?id=1mrqL9rFoYW-4Jc57MsTipkvOTRy_EGfe) | |
2. MSCOCO
* `python3 eval/coco.py` to evaluate coco model by specifying model path there.
| phi | mAP |
| ---- | ---- |
| 0 | 0.334 [weights](https://drive.google.com/open?id=1MNB5q6rJ4TK_gen3iriu8-ArG9jB8aR9), [results](https://drive.google.com/open?id=1U4Bdk4C7aNF7l4mvhh2Oi8mFpttEwB8s) |
| 1 | 0.393 [weights](https://drive.google.com/open?id=11pQznCTi4MaVXqkJmCMcQhphMXurpx5Z), [results](https://drive.google.com/open?id=1NjGr3yG3_Rk1xVCk4sgVelTZNNz_E2vp) |
| 2 | 0.424 [weights](https://drive.google.com/open?id=1_yXrOrY0FDnH-d_FQIPbGy4z2ax4aNh8), [results](https://drive.google.com/open?id=1UQP8kDj7tXHC2bs--Aq8x7w7FkVX4xJD) |
| 3 | 0.454 [weights](https://drive.google.com/open?id=1VnxoBpEQmm0Z2uO3gjhYDeu-rNirba6c), [results](https://drive.google.com/open?id=1uruTEMPhl_JvbA_T9kCdutzeOR3gFX4g) |
| 4 | 0.483 [weights](https://drive.google.com/open?id=1lQvTpnO_mfkHCRpcP28dxU4CWyK3xUzj), [results](https://drive.google.com/open?id=1s4nmgYaPqjbAgDlRF1AVVz6uWKDz7O_i) |
## Test
`python3 inference.py` to test your image by specifying image path and model path there.
