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Pretrained deep learning features for Documents trained on OCR results with Object Detection frameworks

Python 97.94% Shell 2.06%

object_detection_ocr's Introduction

Pretrained document features for document OCR, classification and segmentation

The objective of this repository is to develop pretrained features for document images to be used in document classification, segmentation, OCR and analysis. The pretrained features are being trained upon OCR results from a OCR technology, such as Tesseract.

PDF paper

Features

  • Python 2 and Python 3 support

  • Tensorflow and CNTK support

To run the training with CNTK, activate the Python environment source activate keras-tf-py27 and set backend value to tensorflow in ~/.keras/keras.json.

To run the training with CNTK, activate the Python environment source activate cntk-py27 and set backend value to cntk in ~/.keras/keras.json.

  • Multi-GPUs support

To enable parallel computing with multi-gpus:

python train.py -p

For CNTK, start parallel workers to use all GPUs:

mpiexec --npernode 4 python train.py -p
  • TensorBoard visualization Train and validation loss, objectness accuracy per layer scale, class accuracy per layer scale, regression accuracy, object mAP score, target mAP score, original image, objectness map, multi layer detections, detections after non-max-suppression, target and groundtruth.

Install requirements

Create cntk-py35 and cntk-py27 Conda environments following their specs.

Build: ../../configure --with-swig=/usr/local/swig-3.0.10 --with-py35-path=$HOME/anaconda3/envs/cntk-py35 --with-py27-path=$HOME/anaconda3/envs/cntk-py27

Update their environments to add Keras and other libraries for the current code :

conda env update --file cntk-py27.yml
conda env update --file cntk-py35.yml
  • Tensorflow and Python 2.7
conda env update --file keras-tf-py27.yml
  • Tensorflow and Python 3.5
conda env update --file keras-tf-py35.yml
  • HDF5 to save weights with Keras
sudo apt-get install libhdf5-dev

Run

Activate one of the Conda environments:

source activate cntk-py27
source activate cntk-py35
source activate keras-tf-py27
source activate keras-tf-py35

For help on available options:

python train.py -h
python3 train.py -h

Using TensorFlow backend/
Using CNTK backend
Selected GPU[3] GeForce GTX 1080 Ti as the process wide default device.
usage: train.py [-h] [-b BATCH_SIZE] [-p] [-e EPOCHS] [-l LOGS] [-m MODEL]
                [-lr LEARNING_RATE] [-s STRIDE_SCALE] [-d DATASET] [-w WHITE]
                [-n] [--pos_weight POS_WEIGHT] [--iou IOU]
                [--nms_iou NMS_IOU] [-i INPUT_DIM] [-r RESIZE] [--no-save]
                [--resume RESUME_MODEL]

optional arguments:
  -h, --help            show this help message and exit
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        # of images per batch
  -p, --parallel        Enable multi GPUs
  -e EPOCHS, --epochs EPOCHS
                        # of training epochs
  -l LOGS, --logs LOGS  log directory
  -m MODEL, --model MODEL
                        model
  -lr LEARNING_RATE, --learning_rate LEARNING_RATE
                        learning rate
  -s STRIDE_SCALE, --stride_scale STRIDE_SCALE
                        Stride scale. If zero, default stride scale.
  -d DATASET, --dataset DATASET
                        dataset
  -w WHITE, --white WHITE
                        white probability for MNIST dataset
  -n, --noise           noise for MNIST dataset
  --pos_weight POS_WEIGHT
                        weight for positive objects
  --iou IOU             iou treshold to consider a position to be positive. If
                        -1, positive only if object included in the layer
                        field
  --bb_positive BB_POSITIVE
                        Possible values: iou-treshold, in-anchor, best-anchor
  --nms_iou NMS_IOU     iou treshold for non max suppression
  -i INPUT_DIM, --input_dim INPUT_DIM
                        network input dim
  -r RESIZE, --resize RESIZE
                        resize input images
  --no-save             save model and data to files
  --resume RESUME_MODEL
  --n_cpu N_CPU         number of CPU threads to use during data generation

OCR Training

Toy dataset with MNIST "ocr_mnist"

Train image recognition of digits on a white background (inverted MNIST images):

Command Obj acc Class acc Reg acc Obj mAP
python train.py 100 99.2827 1.60e-10 99.93
With noise python train.py -n 99.62 98.92 4.65e-6 98.41

With stride 12 instead of default 28:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -s 6 --iou .15 96.37 36.25 0.010 99.97 100
python train.py -s 6 --iou .2 98.42 28.56 0.012 99.75 100
python train.py -s 6 --iou .25 97.05 36.42 0.015 99.52 100
python train.py -s 6 --iou .3 98.35 92.78 0.0013 99.88 100
python train.py -s 6 --iou .35 98.99 83.72 0.0069 99.22  100
python train.py -s 6 --iou .4 98.70 94.96 0.0066 98.37  100
python train.py -s 6 --iou .5 96.71 95.46 0.0062 91.09 95.71
python train.py -s 6 --iou .6 99.92 98.23 4.8e-05 51.80 54.32
python train.py -s 6 --iou .8 99.90 97.90 7.67e-05 8.5 10.63
python train.py -s 6 --iou .95 99.94 97.27 3.7-07 10.80 12.21
python train.py -s 6 --iou .99 99.91 97.66 7.06e-07 9.3 11.71

With stride 4:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -s 2 --iou .2 98.51 72.71 0.034 99.99 100
python train.py -s 2 --iou .25 98.63 78.53 0.018 100 100
python train.py -s 2 --iou .3 97.88 94.54 0.0098 99.89 100
python train.py -s 2 --iou .4 96.85 97.41 0.0098 99.93 100
python train.py -s 2 --iou .5 94.14 98.81 0.0099 99.61 100
python train.py -s 2 --iou .6 99.80 98.57 0.00031 99.93 100
python train.py -s 2 --iou .7 99.64 98.21 0.0016 99.77 100
python train.py -s 2 --iou .8 100 98.19 1.7e-8 82.24 100
python train.py -s 2 --iou .8 -e 30 99.98  99.35  1.73e-9  91.05  100

Train on scale ranges [14-28]:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -r 14-28 -s 6 --iou .25 -e 30 99.10 89.37 0.0017 99.58 100

With bigger net:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -m CNN_C64_C128_M2_C256_D -s 6 --iou .5 99.59 98.02 0.00078 92.32 94.89
python train.py -m CNN_C64_C128_M2_C256_D -s 6 --iou .4 99.17 97.23 0.0047 99.79 100
python train.py -m CNN_C64_C128_M2_C256_D -s 6 --iou .3 99.74 96.84 0.00043 100 100
python train.py -m CNN_C64_C128_M2_C256_D -s 6 --iou .2 97.57 91.14 0.0016 99.98 100
python train.py -m CNN_C64_C128_M2_C256_D -s 6 --iou .15 98.02 83.85 0.0083 99.95 100
python train.py -m CNN_C64_C128_M2_C256_D -s 2 --iou .5 99.80 98.87 0.00053 100 100
python train.py -m CNN_C64_C128_M2_C256_D -s 2 --iou .25 99.48 95.78 0.00054 100 100
python train.py -r 14-28 -m CNN_C64_C128_M2_C256_D -s 6 --iou .25 -e 30 96.58 91.42 0.0045 99.85 100

Train on scale 56x56:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -r 56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D 99.98 99.22 7.4e-09 99.97 100
python train.py -r 56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .2 98.86 78.63 0.011 99.89 100
python train.py -r 56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .3 99.36 94.60 0.0036 99.97 100
python train.py -r 56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .4 99.23 91.11 0.048 100 100

Train for two stage networks (scales 28 and 56):

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -r 28,56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 99.99/1.0 98.62/96.69 1.06e-08/4.18e-05 99.97 100
python train.py -r 28,56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 -s 6 -e 50 99.51/97.76 89.83/95.22 0.0048/0.016 99.44 100
python train.py -r 28,56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 -s 4 -e 30 99.39/97.46 85.21/92.19 0.0054/0.022 99.64  100

Train on scale ranges [28-56], two stages [14-28,28-56] and [14, 56]:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -r 28-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .25 -e 30 98.99 93.92 0.0018 99.89 100
python train.py -r 14-28,28-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 -s 6 --iou .25 -e 30 98.92/98.04 64.06/91.08 0.0037/0.0056 98.82 99.90
python train.py -r 14-28,28-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 -s 6 --iou .2 -e 30 98.57/97.73 58.30/79.84 0.0058/0.0036 98.31 99.90
python train.py -r 14-28,28-56 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2 -s 6 --iou .25 -e 30 99.10 / 98.16 93.64 / 95.28 0.0016 / 0.0014 98.42 99.93
python train.py -r 14-28,28-56 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2 -s 6 --iou .25 -e 50 99.26 / 98.78 93.91 / 94.02 0.0010 / 0.0014 98.81 99.93
python train.py -r 14-28,28-56 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2 -s 6 --iou .2 -e 50 99.05/98.05 89.88/91.97 0.0021/0.0022 99.11 99.97
python train.py -r 14-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .02 -e 30 97.58 30.17 0.10 75.07 100
python train.py -r 14-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .05 -e 30 97.92 53.20 0.027 75.49 100
python train.py -r 14-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .1 -e 30 97.82 58.44 0.0057 87.45 92.67
python train.py -r 14-56 -m CNN_C32_C64_M2_C64_C64_M2_C128_D -s 6 --iou .2 -e 30 98.82 79.23 0.0010 72.36 75.78

Train on lower resolution (digit resize parameter):

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -e 30 -r 14 -m CNN_C32_C64_C128_D 100 99.04 2.2-12 99.91 100
python train.py -e 30 -r 14 -m CNN_C32_C64_C128_D -s 4 97.12 94.50 0.012 99.91 100
python train.py -e 30 -r 14 -m CNN_C32_C64_C128_C 100 98.75 1.9-05 97.02 100
python train.py -e 30 -r 14 -m CNN_C32_C64_C128_C -s 4 98.00 91.69 0.023 93.87 100
python train.py -e 30 -r 7-14 --iou .2 -m CNN_C32_C64_C128_D 99.99 96.78 8.4e-5 99.85 100
python train.py -e 30 -r 7-14 --iou .2 -m CNN_C32_C64_C128_D -s 4 98.58 73.07 0.0087 98.61 100
python train.py -e 30 -r 7-14 --iou .25 -m CNN_C32_C64_C128_D -s 4 99.07 75.34 0.012 98.98 100
python train.py -e 30 -r 7-14 --iou .2 -m CNN_C32_C64_C128_C 99.31 93.61 0.0035 92.52 100
python train.py -e 30 -r 7-14 --iou .2 -m CNN_C32_C64_C128_C -s 4 97.22 24.87 0.0060 97.68 100
python train.py -e 30 -r 7-14 --iou .2 -m CNN_C32_C64_C128_C2 -s 4 98.49 47.93 0.0088 98.91 100
python train.py -e 30 -r 7-28 -s 6 -m CNN_C32_C64_C64_Cd64_C128_D --iou .02 96.51 24.42 0.12 64.43 66.47
python train.py -e 30 -r 7-28 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .2 99.12 91.01 0.0040 84.87 77.18
python train.py -e 30 -r 7-28 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .15 98.40 77.86 0.029 88.68 85.71
python train.py -e 30 -r 7-28 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .1 98.20 56.96 0.086 87.51 95.34
python train.py -e 30 -r 7-28 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .05 -lr 0.001 97.71 38.91 0.032 77.98 100
python train.py -e 30 -r 7-28 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .02 --lr 0.0001 96.79 18.59 0.10 77.28 100
python train.py -e 30 -r 7-28 -s 3 -m CNN_C32_C64_C64_Cd64_C128_D --iou .1 97.47 73.70 0.010 87.19 95.45
python train.py -e 30 -r 7-28 -s 3 -m CNN_C32_C64_C64_Cd64_C128_D --iou .2 99.08 92.84 0.0074 81.01  76.47
python train.py -e 50 -r 7-28 -s 3 -m CNN_C32_C64_C64_Cd64_C128_D --iou .15 98.71 88.02 0.0046 87.79 84.76
python train.py -e 50 -r 7-28 -s 3 -m CNN_C32_C64_C64_Cd64_C128_D --iou .1  97.97  79.19  0.0096 89.17 95.24

Train on larger images (1000 or 1500 rather than 700):

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -e 30 -i 1000 -r 7-14 --iou .2 -m CNN_C32_C64_C128_D -s 4 98.80 52.92 0.0081 98.78 100
python train.py -e 30 -i 1000 -r 7-14 --iou .2 -m CNN_C32_C64_C128_C -s 4 98.24 20.36 0.011 97.46 100
python train.py -e 30 -i 1500 -r 7-14 --iou .2 -m CNN_C32_C64_C128_D -s 4 98.61 47.04 0.0076 98.36 100
python train.py -e 30 -i 1000 -r 7-28 --iou .2 -m CNN_C32_C64_C64_Cd64_C128_D -s 4 98.93 89.25 0.0031 81.39 76.23
python train.py -e 30 -i 1500 -r 7-28 --iou .2 -m CNN_C32_C64_C64_Cd64_C128_D -s 3 -b 1 99.04 91.46 0.0063 82.33 76.95
python train.py -e 50 -i 1500 -r 7-28 --iou .2 -m CNN_C32_C64_C64_Cd64_C128_D -s 3 -b 1 98.78 91.20  0.011 82.93 76.38
python train.py -e 50 -i 1500 -r 7-28 --iou .2 -m CNN_C32_C64_C64_Cd64_C128_D -s 4 -b 1 98.96 92.69 0.0015 80.29 76.97

OCR Dataset "ocr_documents"

Create a document configuration file document.conf in JSON specifying the directory in which document files are in JPG:

{
  "directory": "/sharedfiles/ocr_documents",
  "namespace": "ivalua.xml",
  "page_tag": "page",
  "char_tag": "char",
  "x1_attribute": "x1",
  "y1_attribute": "y1",
  "x2_attribute": "x2",
  "y2_attribute": "y2"
}

Use Tesseract OCR to produce the XML files:

sudo apt-get install tesseract-ocr tesseract-ocr-fra
python datasets/ocr_documents_preprocess.py

Get document statistics with python ocr_documents_statistics.py.

By default, input size is 700, this means 3500x2500 input images will be cropped to 700x420 :

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -e 50 -d ocr_documents -s 2 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.15 97.00/97.76 69.11/71.78 0.027/0.016 58.82 91.22
python train.py -e 50 -d ocr_documents -s 2 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.2 97.89/98.44 75.39/72.75 0.020/0.011 68.09 84.47
python train.py -e 50 -d ocr_documents -s 2 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.25 98.19 81.43 0.014 64.69 65.40
python train.py -e 50 -d ocr_documents -s 3 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.15 97.52/ 97.58 72.18/77.03 0.028/0.015 67.05 86.07
python train.py -e 50 -d ocr_documents -s 3 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.2 98.24/98.25 79.01/79.47 0.019/0.10 66.25 78.15
python train.py -e 50 -d ocr_documents -s 3 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.25 98.60/98.90 80.17/78.93 0.015/0.0075 62.71 66.42
python train.py -e 50 -d ocr_documents -s 4 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.15 97.90/97.50 72.05/74.58 0.029/0.017 62.87 89.77
python train.py -e 50 -d ocr_documents -s 4 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.2 98.42/97.99 78.35/79.15 0.021/0.012 66.30 83.94
python train.py -e 50 -d ocr_documents -s 4 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.25 98.88/98.61 77.64/81.11 0.017/0.0077 60.26 69.35
python train.py -e 50 -d ocr_documents -s 5 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.15 98.47/97.36 70.94/77.87 0.031/0.018 59.33 85.87
python train.py -e 50 -d ocr_documents -s 5 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.2 98.92/97.76 67.94/80.13 0.021/0.014 51.87 77.52
python train.py -e 50 -d ocr_documents -s 5 -m CNN_C32_C64_M2_C64_C64_M2_C128_D_2 --iou 0.25 99.09/98.45 70.41/83.67 0.018/0.0097 44.59 61.57

With more capacity:

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -e 50 -d ocr_documents -s 3 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2 --iou 0.2 (1) 98.45/98.66 83.27/85.42 0.018/0.0097 70.11 78.15

(1) Model Tensorflow wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/2018-05-28_20:03_CNN_C64_C128_M2_C128_C128_M2_C256_D_2.h5

Model CNTK wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/2018-06-04_12:05_CNN_C64_C128_M2_C128_C128_M2_C256_D_2.h5 and wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/2018-06-13_21.37_CNN_C64_C128_M2_C128_C128_M2_C256_D_2.dnn

To train on lower resolution, resize input images to 1000 (downsize by 3.5) and change input size by the same factor, to 200, in order to get 200x120 crops :

Command Obj acc Class acc Reg acc Obj mAP Target mAP
python train.py -e 150 -d ocr_documents -r 1000 -i 200 -s 6 -m CNN_C64_C128_M2_C256_D --iou .25 98.90 34.14 0.013 8.82 29.58
python train.py -e 150 -d ocr_documents -r 1000 -i 200 -s 6 -m CNN_C64_C128_M2_C256_D --iou .2      
python train.py -e 150 -d ocr_documents -r 1000 -i 200 -s 1 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2_S7 --iou 0.2 -b 1 (2) 98.02/99.85 72.54/.00 0.013/0.0017 48.81 69.38
python train.py -e 50 -d ocr_documents -r 1000 -i 200 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .15 98.32 45.78 0.018 36.17 69.74
python train.py -e 50 -d ocr_documents -r 1000 -i 200 -s 4 -m CNN_C32_C64_C128_D --iou .15 96.87 61.79 0.023 46.89 69.08
python train.py -e 50 -d ocr_documents -r 1000 -i 200 -s 4 -m CNN_C32_C64_C128_D --iou .2 97.20 62.90 0.016 42.25 61.84
python train.py -e 150 -d ocr_documents -r 1700 -i 400 -s 6 -m CNN_C64_C128_M2_C256_D --iou .25 98.38 86.83 0.012 31.76 43.46
python train.py -e 150 -d ocr_documents -r 1700 -i 400 -s 6 -m CNN_C64_C128_M2_C256_D --iou .2 97.72 83.86 0.016 42.00 59.83

(2) wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/2018-06-22_13:02_CNN_C64_C128_M2_C128_C128_M2_C256_D_2_S7.h5

For OCR training on full document images:

Command Obj acc Class acc Reg acc
python train.py -e 50 -d ocr_documents_generator -i 2000 -r 2000 -s 3 -m CNN_C64_C128_M2_C128_C128_M2_C256_D_2 --iou 0.2 S3
python train.py -e 50 -d ocr_documents_generator --n_cpu 8 -i 1000 -r 1000 -s 4 -m CNN_C32_C64_C64_Cd64_C128_D --iou .15 S3
python train.py -e 50 -d ocr_documents_generator --n_cpu 8 -i 1000 -r 1000 -s 4 -m CNN_C32_C64_C128_D --iou .2 (3) 98.49 69.11 0.0158
python train.py -e 50 -d ocr_documents_generator --n_cpu 8 -i 1500 -r 1500 -s 4 -m CNN_C32_C64_C128_D --iou .2 V1 Good

(3) wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/2018-06-25_15:44_CNN_C32_C64_C128_D.h5

Classification Training

Cats and dogs dataset

Download dataset from https://www.kaggle.com/c/dogs-vs-cats/data

unzip /sharedfiles/train.zip -d /sharedfiles
./datasets/cls_dogs_vs_cats.sh /sharedfiles/train
Command Class acc
python train.py -d cls_dogs_vs_cats -i 150 -m VGG16_D256 -lr 0.001 -b 16 91.82

Tiny ImageNet dataset

Download dataset

wget http://cs231n.stanford.edu/tiny-imagenet-200.zip -P /sharedfiles
unzip /sharedfiles/tiny-imagenet-200.zip -d /sharedfiles/
./datasets/cls_tiny_imagenet_convert.sh /sharedfiles/tiny-imagenet-200
python train.py -d cls_tiny_imagenet -i 150 -m VGG16_D4096_D4096 -lr 0.001 -b 64 -e 150 -p

RVL-CDIP dataset

wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/rvl-cdip.tar.gz -P /sharedfiles
# aws s3 cp s3://christopherbourez/public/rvl-cdip.tar.gz /sharedfiles/
mkdir /sharedfiles/rvl_cdip
tar xvzf /sharedfiles/rvl-cdip.tar.gz -C /sharedfiles/rvl_cdip
./datasets/cls_rvl_cdip_convert.sh /sharedfiles/rvl_cdip
# remove corrupted tiff
rm /sharedfiles/rvl_cdip/test/scientific_publication/2500126531_2500126536.tif
Command Class acc
python train.py -d cls_rvl_cdip -i 150 -m VGG16_D4096_D4096 -lr 0.0001 -b 64 -e 25 -p 90.2

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