https://github.com/Shun14/TextBoxes_plusplus_Tensorflow

Textboxes_plusplus implementation with Tensorflow (python)
https://github.com/Shun14/TextBoxes_plusplus_Tensorflow

tensorflow textboxes

Last synced: 2 months ago
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Textboxes_plusplus implementation with Tensorflow (python)

• Host: GitHub
• URL: https://github.com/Shun14/TextBoxes_plusplus_Tensorflow
• Owner: Shun14
• Created: 2018-11-28T09:11:27.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-11-16T23:06:43.000Z (about 4 years ago)
• Last Synced: 2024-08-02T11:15:32.310Z (6 months ago)
• Topics: tensorflow, textboxes
• Language: Python
• Size: 323 KB
• Stars: 81
• Watchers: 3
• Forks: 25
• Open Issues: 11
• Metadata Files:
  • Readme: README.md

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README

        
# TextBoxes++-TensorFlow

TextBoxes++ re-implementation using tensorflow.

This project is greatly inspired by [slim project](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim)

And many functions are modified based on [SSD-tensorflow project](https://github.com/balancap/SSD-Tensorflow)

Author:

	Zhisheng Zou [email protected]

# pretrained model 

1. [Google drive](https://drive.google.com/open?id=1kkRyVrx9iFtwEar6OJBKWNVyTLSYsF28)

# environment

` python2.7/python3.5 ` 

`tensorflow-gpu 1.8.0`

`at least one gpu`

# how to use

1. Getting the  xml file like this [example xml](./demo/example/image0.xml) and put the image together because we need the format like this [standard xml](./demo/example/standard.xml)

   1. picture format: *.png or *.PNG

2. Getting the xml and flags

   ensure the XML file is under the same directory as the corresponding image.execute the code: [convert_xml_format.py](./tools/convert_xml_format.py)

   1. `python tools/convert_xml_format.py -i in_dir  -s split_flag -l save_logs -o output_dir` 

   2. in_dir means the absolute directory which contains the pic and xml

   3. split_flag means whether or not to split the datasets

   4. save_logs means whether to save train_xml.txt

   5. output_dir means where to save xmls

3. Getting the tfrecords

   1. `python gene_tfrecords.py --xml_img_txt_path=./logs/train_xml.txt --output_dir=tfrecords` 

   2. xml_img_txt_path like this [train xml](./logs/train_xml.txt)

   3. output_dir means where to save tfrecords

4. Training

   1. `python train.py --train_dir =some_path --dataset_dir=some_path --checkpoint_path=some_path`

   2. train_dir  store the checkpoints when training

   3. dataset_dir store the tfrecords for training

   4. checkpoint_path store the model which needs to be fine tuned

5. Testing

   1. `python test.py -m /home/model.ckpt-858 -o test`

   2. -m which means the model

   3. -o which means output_result_dir

   4. -i which means the test img dir

   5. -c which means use which device to run the test

   6. -n which means the nms threshold

   7. -s which means the score threshold

# Note:

1. when you are training the model, you can run the eval_result.py to eval your model and save the result