yanbingjiang / inception-v3-retrain-fast-with-tfrecord

Follow Official Tensorflow's retrain.py, which is an example script that shows how one can adapt a pretrained network for other classification problems. This repository improved with quick data read and process to make the training more efficient.

inception V3 Retrain Fast Input With TFrecord QueueRunner

Transfer learning for inception V3 is popular tutorial from offcial Tensorflow, named as retrain.py, which is an example script that shows how one can adapt a pretrained network for other classification problems.

This repository improved with quick data read and process to make the training more efficient by utilizing TFRecord.

Compatibility

Tested under Tensorflow 1.6.0/1.9.0/1.12.0 with GPU support under Python 3.6.0 under Windows.

Description of the Code

This repository splited the training process into three steps:

• Create the bottlenecks in .txt format (create_image_bottleneck.py)
• Convert bottlenecks into .tfrecord format (tfrec_data.py / tfrec_data_threads.py)
• Training and Evaluation Process nearly identical to retrain.py

Getting Started and Usage

Requirements

• Tensorflow with Tensorboard, Numpy, six, Panda
• Followe the file named requirements.txt

pip3 install -r requirements.txt # For Python3, 'pip3' may vary to 'pip' depends on your machine

Data Preparation

Follow http://download.tensorflow.org/example_images/flower_photos.tgz to download the flower images and extract.

Create Bottlenecks

python create_image_bottlenecks.py --image_dir=<path to training images> --bottleneck_dir=<Path to cache bottleneck>

Example

python create_image_bottlenecks.py  # Run as Default
python create_image_bottlenecks.py --image_dir=./flowers_photo --bottleneck_dir=./bottleneck # Run with personalized settings

Convert Bottlenecks to TFRecords Format

When creating the TFRecords, the dataset needs to be splited into training set, validation set and testing set as well. The same work as 'create_image_list' function in retrain.py. Thus, percentage of validation set and percentage of testing set needs to specified. Number of shards is a parameter to specify how many tfrecord file you would like to have to one set. For example, if num_shards = 5, it will divide training set into 5 tfrecord file evenly, so as validation set and testing set.

• Option 1: Covert Data Sequetially, for flower set is fast enough

python tfrec_data.py --bottleneck_dir=<Path to cache bottleneck> --tfrecord_dir=<Path to store tfrecord bottleneck> --validation_percentage=<validation %> --testing_percentage=<testing %> --num_shards=<number of tfrecords files>

Example

python tfrec_data.py  # Run as Default with validation 10% testing 10% and 5 shards
python tfrec_data.py --bottleneck_dir=./bottleneck --tfrecord_dir=./bottleneck_tf --validation_percentage=10 --10 --num_shards=5

• Option 2: Covert Data in multi-threads behavious, for larger dataset convertion Note that number of shards mod number of threads needs to qual to zero (num_shards % num_threads == 0) to make it work.

python tfrec_data_thread.py --bottleneck_dir=<Path to cache bottleneck> --tfrecord_dir=<Path to store tfrecord bottleneck> --validation_percentage=<validation %> --testing_percentage=<testing %> --num_shards=<number of tfrecords files> --num_threads=<number of threads>

Example

python tfrec_data_thread.py  # Run as Default with validation 10% testing 10% , 5 shards and 5 threads
python tfrec_data_thread.py --bottleneck_dir=./bottleneck --tfrecord_dir=./bottleneck_tf --validation_percentage=10 --testing_percentage=10 --num_shards=5 --num_threads=5

Start Training!

This training will utlize the TFRecord data only instead of .txt file as stated. As introcued in the official tutorial, only the last fully connected layer is trained. In order to train, learning rate, training steps, validation percentage, testing percentage and so on need to be specified.

python retrain_attempt_v3_tfrec.py --image_dir=<Path to original images> --tfrecord_dir=<Path to store tfrecord bottleneck> --output_graph=<Where to save the trained graph> --output_labels=<Where to save the trained graph's labels> --how_many_training_steps =<# of training steps> --learning_rate=<learning rate> --validation_percentage=<validation %> --testing_percentage=<testing %> --num_shards=<number of tfrecords files>

Example

python retrain_attempt_v3_tfrec.py --image_dir=./flower_photos  # Run as Default flower photos in the same directory
python retrain_attempt_v3_tfrec.py --image_dir=./flower_photos --validation_percentage=10 --testing_percentage=10 --num_shards=5 # Run as Default flower photos in the same directory with specified validation/testing percentage and number of shards

Testing Results

Environment: Windows 8.1 / CPU Intel(R) Core(TM) i7-5500U / GPU GeForce 940M

Settings: 4000 Training Steps, 10% Validation, 10% Testing, Learning Rate = 0.001, periodical eval step size = 10

• Original retrain.py: 728 seconds
• This repository:      163 seconds

Author

• Yanbing Jiang

License

This project is licensed under the MIT License - see the LICENSE.md file for details