tensorflow-svhn

• Background
• File Structure
• Usage
• Accuracy

Background

This project is based on tensorflow 1.0, and python 2.7.

I spent quite a few days trying to improve the accuracy of this model, some cannot work, and some can. I have achieved 4.8% error rate on sequencial digits recognition. As a beginner of machine learning, it's quite good for me. But there are still quite a lot modern net structure I haven't use, so I believe it should be able to achieve even higher accuracy.

File Structure

• ckpt/      #store checkpoint files
• train_data/
  • train/      #extracted from train.tar.gz
  • test/        #extracted from test.tar.gz
  • extra/      #extracted from extra.tar.gz
  • full_train_imgs.tfrecords      #generated using svhn_data.py
  • full_test_imgs.tfrecords
  • full_extra_imgs.tfrecords
• digit_struct.py      #data structure for reading original images
• svhn_data.py       #convert images to tfrecord files
• svhn.py                 #model, training operation, loss operation
• svhn_input.py      #generate input queue for training and evaluation
• svhn_train.py
• svhn_eval.py
• multi_digit_reader.py

Usage

1. Download train.tar.gz, test.tar.gz, extra.tar.gz
2. Extract them into /train_data
3. Run svhn_data.py to generate tfrecord files
4. The data is ready now. You can run svhn_train.py to train it from start, or copy everything from ckpt-95.1%-acc/ to ckpt/(if there is no ckpt/ folder, create one), and run svhn_eval.py to get the model accuracy
5. If you want to train the model from start, make sure there is nothing in ckpt/, or it will load the ckeckpoints from ckpt/. The checkpoint is saved in train_data/, if you want to continue from your last training, then just put your last checkpoint into ckpt/
6. Run python multi_digit_reader.py image-name.png to read a complete image. This is not accurate at all, I'm trying to come up with a better way.

Accuracy

|without extra images(70K training images set) | 76% |
|use extra images(600K training+extra images set) | 86% |
|extra + 6 conv + 1 fc | 89.8% |
|extra + 6 conv + 2 fc | 91.2% |
|extra + 7 conv + 2 fc | 92.2% |
|extra + 7 conv + 2 fc + densely connect | 92.2% |
|extra + 8 conv + 2 fc | cannot train |
|extra + 7 conv + 2 fc + inception block | cannot train |
|extra + 7 conv + 2 fc + spatial transformer | cannot train |
|extra + 7 conv + 2 fc + increase number of params | 93.3% |
|extra + 7 conv + 2 fc + increase number of params + bacth normalization | 94.5% |
|extra + 7 conv + 2 fc + increase number of params + bacth normalization | 94.5% |
|extra + 7 conv + 2 fc + increase number of params + bacth normalization + clear some comments(???) | 95.1% |
|extra + 7 conv + 2 fc + increase number of params + bacth normalization + max-avg pooling | 95.2% |
|Update Nvidia driver from 375 -> 381 | 95.6% |
(WTF???)