# Keras-FasterRCNN

**Repository Path**: liu_hui_huang/Keras-FasterRCNN

## Basic Information

- **Project Name**: Keras-FasterRCNN
- **Description**: Keras实现faster rcnn
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2019-12-15
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Keras-FasterRCNN
Keras implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.<br/>
cloned from [https://github.com/yhenon/keras-frcnn/](https://github.com/yhenon/keras-frcnn/)

## UPDATE:
- supporting inception_resnet_v2
  - for use inception_resnet_v2 in keras.application as feature extractor, create new inception_resnet_v2 model file using Transper/export_imagenet.py
  - if use original inception_resnet_v2 model as feature extractor, you can't load weight parameter on faster-rcnn 

## USAGE:
- Both theano and tensorflow backends are supported. However compile times are very high in theano, and tensorflow is highly recommended.
- `train_frcnn.py` can be used to train a model. To train on Pascal VOC data, simply do:
`python train_frcnn.py -p /path/to/pascalvoc/`. 
- the Pascal VOC data set (images and annotations for bounding boxes around the classified objects) can be obtained from: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
- simple_parser.py provides an alternative way to input data, using a text file. Simply provide a text file, with each
line containing:

    `filepath,x1,y1,x2,y2,class_name`

    For example:

    /data/imgs/img_001.jpg,837,346,981,456,cow
    
    /data/imgs/img_002.jpg,215,312,279,391,cat

    The classes will be inferred from the file. To use the simple parser instead of the default pascal voc style parser,
    use the command line option `-o simple`. For example `python train_frcnn.py -o simple -p my_data.txt`.

- Running `train_frcnn.py` will write weights to disk to an hdf5 file, as well as all the setting of the training run to a `pickle` file. These
settings can then be loaded by `test_frcnn.py` for any testing.

- test_frcnn.py can be used to perform inference, given pretrained weights and a config file. Specify a path to the folder containing
images:
    `python test_frcnn.py -p /path/to/test_data/`
- Data augmentation can be applied by specifying `--hf` for horizontal flips, `--vf` for vertical flips and `--rot` for 90 degree rotations



## NOTES:
- config.py contains all settings for the train or test run. The default settings match those in the original Faster-RCNN
paper. The anchor box sizes are [128, 256, 512] and the ratios are [1:1, 1:2, 2:1].
- The theano backend by default uses a 7x7 pooling region, instead of 14x14 as in the frcnn paper. This cuts down compiling time slightly.
- The tensorflow backend performs a resize on the pooling region, instead of max pooling. This is much more efficient and has little impact on results.


## Example output:

![ex1](http://i.imgur.com/7Lmb2RC.png)
![ex2](http://i.imgur.com/h58kCIV.png)
![ex3](http://i.imgur.com/EbvGBaG.png)
![ex4](http://i.imgur.com/i5UAgLb.png)

## ISSUES:

- If you get this error:
`ValueError: There is a negative shape in the graph!`    
    than update keras to the newest version

- Make sure to use `python2`, not `python3`. If you get this error:
`TypeError: unorderable types: dict() < dict()` you are using python3

- If you run out of memory, try reducing the number of ROIs that are processed simultaneously. Try passing a lower `-n` to `train_frcnn.py`. Alternatively, try reducing the image size from the default value of 600 (this setting is found in `config.py`.

## Reference
[1] [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, 2015](https://arxiv.org/pdf/1506.01497.pdf) <br/>
[2] [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, 2016](https://arxiv.org/pdf/1602.07261.pdf) <br/>
[3] [https://github.com/yhenon/keras-frcnn/](https://github.com/yhenon/keras-frcnn/)