/SVHNClassifier-PyTorch

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (http://arxiv.org/pdf/1312.6082.pdf)

Primary LanguageJupyter NotebookMIT LicenseMIT

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