An implementation of Q Zhao et al., "M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network", 2019 using TensorFlow.
COCO 2017 dataset is used for training.
To be released.
- Python 3.6
- TensorFlow 1.8
You can download the trained model [weights_2019-01-31].
To be released before long.
$ python demo.py --inputs <image_path> --model_path <model_path>
Download COCO (2017) dataset from http://cocodataset.org and process them:
$ python mscoco/process.py --image_dir <image_dir> --annotation_path <annotation_dir> --output_dir <output_dir>
$ mkdir weights
$ python train.py --image_dir <image_dir> --label_dir <label_dir>
ImaneNet pre-training is not used.
cf. Kaiming He et al., "Rethinking ImageNet Pre-training", 2018
To be written.
MIT License
Copyright (c) 2019 tadax
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.