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A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (http://arxiv.org/pdf/1312.6082.pdf)
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Latest commit 5f062e5 Mar 18, 2019

README.md

SVHNClassifier-PyTorch

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

If you're interested in C++ inference, move HERE

Results

Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Accuracy
54000 GTX 1080 Ti 512 0.16 100 625 0.9 ~1700 95.65%

Sample

$ python infer.py -c=./logs/model-54000.pth ./images/test-75.png
length: 2
digits: 7 5 10 10 10

$ python infer.py -c=./logs/model-54000.pth ./images/test-190.png
length: 3
digits: 1 9 0 10 10

Loss

Requirements

  • Python 3.6

  • torch 1.0

  • torchvision 0.2.1

  • visdom

    $ pip install visdom
    
  • h5py

    In Ubuntu:
    $ sudo apt-get install libhdf5-dev
    $ sudo pip install h5py
    
  • protobuf

    $ pip install protobuf
    
  • lmdb

    $ pip install lmdb
    

Setup

  1. Clone the source code

    $ git clone https://github.com/potterhsu/SVHNClassifier-PyTorch
    $ cd SVHNClassifier-PyTorch
    
  2. Download SVHN Dataset format 1

  3. Extract to data folder, now your folder structure should be like below:

    SVHNClassifier
        - data
            - extra
                - 1.png 
                - 2.png
                - ...
                - digitStruct.mat
            - test
                - 1.png 
                - 2.png
                - ...
                - digitStruct.mat
            - train
                - 1.png 
                - 2.png
                - ...
                - digitStruct.mat
    

Usage

  1. (Optional) Take a glance at original images with bounding boxes

    Open `draw_bbox.ipynb` in Jupyter
    
  2. Convert to LMDB format

    $ python convert_to_lmdb.py --data_dir ./data
    
  3. (Optional) Test for reading LMDBs

    Open `read_lmdb_sample.ipynb` in Jupyter
    
  4. Train

    $ python train.py --data_dir ./data --logdir ./logs
    
  5. Retrain if you need

    $ python train.py --data_dir ./data --logdir ./logs_retrain --restore_checkpoint ./logs/model-100.pth
    
  6. Evaluate

    $ python eval.py --data_dir ./data ./logs/model-100.pth
    
  7. Visualize

    $ python -m visdom.server
    $ python visualize.py --logdir ./logs
    
  8. Infer

    $ python infer.py --checkpoint=./logs/model-100.pth ./images/test1.png
    
  9. Clean

    $ rm -rf ./logs
    or
    $ rm -rf ./logs_retrain
    
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