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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

<table> <tr> <th>Steps</th> <th>GPU</th> <th>Batch Size</th> <th>Learning Rate</th> <th>Patience</th> <th>Decay Step</th> <th>Decay Rate</th> <th>Training Speed (FPS)</th> <th>Accuracy</th> </tr> <tr> <td> <a href="https://drive.google.com/open?id=1DSg3F5GpouEvU9n7YSPdUKH1CSmkdwSw"> 54000 </a> </td> <td>GTX 1080 Ti</td> <td>512</td> <td>0.16</td> <td>100</td> <td>625</td> <td>0.9</td> <td>~1700</td> <td>95.65%</td> </tr> </table>

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

Clone the source code

$ git clone https://github.com/potterhsu/SVHNClassifier-PyTorch
$ cd SVHNClassifier-PyTorch

Download SVHN Dataset format 1

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

(Optional) Take a glance at original images with bounding boxes
```
Open `draw_bbox.ipynb` in Jupyter
```

Convert to LMDB format

$ python convert_to_lmdb.py --data_dir ./data

(Optional) Test for reading LMDBs

Open `read_lmdb_sample.ipynb` in Jupyter

Train

$ python train.py --data_dir ./data --logdir ./logs

Retrain if you need

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

Evaluate

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

Visualize

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

Infer

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

Clean

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