Target Detection: FPN_tensorflow training their own data sets

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This link: https://blog.csdn.net/lianggyu/article/details/100096400

Code links: https://github.com/DetectionTeamUCAS/FPN_Tensorflow

This article was produced voc format data sets, research target detection based tensorflow training FPN network. VOC production format of the data set is not carried in detail, with a lot of online tutorials. Anaconda employed herein to build the environment, and detect the target gun, knife two categories described as an example.

1, built environment

Installation depends

python3.5 (anaconda recommended)

cuda9.0 (if you want to use cuda8, set CUDA9 = False in cfgs.py file.)

opencv-python

tensorflow-plot

tensorflow == 1.10

Download the pre-training model

Download resnet50_v1 , resnet101_v1 pre-training model, which put $ PATH_ROOT / data / pretrained_weights.

Compile

cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace

2 format, the data set

|—— data/VOCdevkit
|     |——train
|        |——Annotation
|        |——JPEGImages
|     |——test
|        |——Annotation
|        |——JPEGImages

3, cfgs.py modify the configuration

Select a network based on a folder ($ PATH_ROOT / libs / configs /), copy the contents of the corresponding profile and the cover cfgs.py.

The actual data set (in this example 2 is the number of categories, set a custom name for the data "train"), to modify the following lines, to selectively modify the training parameters, such as MOMENTUM, LR, DECAY_STEP, MAX_ITERATION etc.

test_annotate_path = '$PATH_ROOT/data/VOCdevkit/train/Annotations'
DATASET_NAME = 'train'  # 'ship', 'spacenet', 'pascal', 'coco'
CLASS_NUM = 2

Note: Recommended to customize the name of a data set, otherwise an error may occur: OutOfRangeError (see above for traceback): PaddingFIFOQueue '1_get_batch / batch / padding_fifo_queue' is closed and has insufficient elements

4, label_dict.py changes

In /lib/label_name_dict/label_dict.py file, the code modified as follows:

class_names = [
        'back_ground', 'gun', 'knife']

classes_originID = {
    'gun': 1, 'knife': 2}

if cfgs.DATASET_NAME == 'train':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'gun': 1,
        'knife': 2
    }

5, read_tfrecord.py changes

$ PATH_ROOT / data / io / read_tfrecord.py line 76, add custom data set name.

if dataset_name not in ['ship', 'spacenet', 'pascal', 'coco','train']:
        raise ValueError('dataSet name must be in pascal, coco spacenet and ship')

6, production tfrecord

cd $PATH_ROOT/data/io/
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/' 
                                   --xml_dir='Annotation'
                                   --image_dir='JPEGImages'
                                   --save_name='train' 
                                   --img_format='.jpg' 
                                   --dataset='pascal'

E.g:

python convert_data_to_tfrecord.py --VOC_dir='/home/manager/FPN_Tensorflow-master/data/VOCdevkit/train/' --save_name='train' --img_format='.bmp' --dataset='train'

7, training python train.py

8、eval.py

If the image format data set does not belong to '.jpg', 'jpeg', '.png', '.tif', '.tiff', modifying eval.py 119 rows. Example used in this example is bmp format

test_imgname_list = [item for item in os.listdir(eval_dir)
                          if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff','.bmp'))]

cd $PATH_ROOT/tools
python eval.py --eval_imgs='/PATH/TO/IMAGES/'  
               --annotation_dir='/PATH/TO/TEST/ANNOTATION/'
               --GPU='0'

E.g

python eval.py --eval_imgs='/home/FPN_Tensorflow-master/data/VOCdevkit/test/JPEGImages/' --annotation_dir='/home/FPN_Tensorflow-master/data/VOCdevkit/test/Annotations/' --GPU='5'

9、Demo

If the image format data set does not belong to '.jpg', 'jpeg', '.png', '.tif', '.tiff', modifying eval.py 84 rows.

test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir)
                             if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff', '.bmp'))]

cd $PATH_ROOT/tools
python inference.py --data_dir='/PATH/TO/IMAGES/' 
                    --save_dir='/PATH/TO/SAVE/RESULTS/' 
                    --GPU='0'