Metadata-Version: 2.1
Name: efficient-det
Version: 0.0.12
Summary: Efficient-Det Implementation in Keras
Home-page: https://git.hhu.de/zeboz100/efficientdet
Author: Zeynep Boztoprak
Author-email: zeynep.boztoprak@hhu.de
License: UNKNOWN
Project-URL: Bug Tracker, https://git.hhu.de/zeboz100/efficientdet/-/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, !=3.9.*
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: tensorflow (>=2.3.0)
Requires-Dist: tabulate
Requires-Dist: ray[default] (>=1.3.0)
Requires-Dist: ray[tune]
Requires-Dist: matplotlib
Requires-Dist: wandb
Requires-Dist: pillow
Requires-Dist: progressbar2
Requires-Dist: pandas
Requires-Dist: opencv-python

## EfficientDet

Start with following command:

```python
export PYTHONPATH="$PWD/src"
```

All commands should be executed in **efficientdet/**.

#### To test trained model on validation dataset you can use the jupyter notebook or python script in examples/.
```python
For your own implementation set the dataset path and path to the trained model. Default paths are set to efficient/dataset.
```

#### To run all tests:
```python
python3 -m unittest
```

#### To train neural network
```python
python3 src/efficient_det/train.py --dataset_path /path/to/dataset/
```


When using Ray Tune verbose is default set to False. Use W&B for visualization.




