https://github.com/jahongir7174/efficientdet-tf
EfficientDet Implementation using TensorFlow 2
https://github.com/jahongir7174/efficientdet-tf
Last synced: 7 months ago
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EfficientDet Implementation using TensorFlow 2
- Host: GitHub
- URL: https://github.com/jahongir7174/efficientdet-tf
- Owner: jahongir7174
- Created: 2020-09-22T03:49:28.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-22T03:57:25.000Z (about 5 years ago)
- Last Synced: 2025-01-19T00:51:55.217Z (9 months ago)
- Language: Python
- Homepage:
- Size: 195 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# EfficientDet
[EfficientDet](https://arxiv.org/pdf/1911.09070.pdf) implementation for Object Detection using Tensorflow2
#### Generate Anchor (Optional)
* Generate optimal anchor for your dataset using [Anchor Optimizer](https://github.com/martinzlocha/anchor-optimization)
* Change the values of `ratios` and `scales` attribute of `AnchorParameters` class in `utils/util.py`
#### Train
* `cd` project directory
* Run `python setup.py build_ext --inplace`
* Change `classes` variable in `utils\config.py` based on your dataset
* Run `python train.py` for training using `imagenet` pretrained weights
* After training is finished, change `weight_path` variable value to trained weight path, example `weight_path=weights/D4/model1_0.1.h5`
* Run `python train.py` for final training
#### Test
* Run `python test.py`
#### Dataset structure
├── Dataset folder
├── IMAGES
├── 1111.jpg
├── 2222.jpg
├── LABELS
├── 1111.xml
├── 2222.xml
├── train.txt
├── val.txt
#### Note
* xml file should be in PascalVOC format
* for making `train.txt` and `val.txt`, see `VOC2012/ImageSets/Main/train.txt`
#### Reference
* https://github.com/qubvel/efficientnet
* https://github.com/fizyr/keras-retinanet
* https://github.com/xuannianz/EfficientDet
* https://github.com/martinzlocha/anchor-optimization
* https://github.com/google/automl/tree/master/efficientdet
* https://github.com/tensorflow/models/tree/master/research/object_detection