Transfer learning is the unhidden gem in the deep learning world. It allows model creation with significantly reduced training data and time by modifying existing rich deep learning models.

The goal of this framework is to collect best-of-breed approaches, make them developer friendly so they can be used for real-world applications.

Current capabilities:

• Object Detection based on TensorBox - GoogleNet/Overfeat/Rezoom
• Classification based on Tensorflow - InceptionV3

Please note that this is still under active development. See the TODO list in the bottom.

• Tensorflow >= 0.12.1 (Tested on v0.12.1, v1.0 and v1.1)
• nnpack >= 0.1.0 (Tools & Data Portability for Neural Nets)

pip install transferflow

pip install -r requirements.txt
make
make download

First, your training data needs to be formatted like a Standard Scaffold. See NNPack for more details. In this example we'll use a pre-prepared Scaffold test/fixtures/scaffolds/scene_type that has two sets of images:

• 115 images depicting indoor scenes
• 158 images depicting outdoor scenes

We will use this to train a model for Scene Type detection (Indoor VS Outdoor).

from transferflow.classification.trainer import Trainer

# Instantiate Trainer with training data, base model and configuration
scaffold_path = './test/fixtures/scaffolds/scene_type'
base_model_path = './models/inception_v3'
trainer = Trainer(base_model_path, scaffold_path, num_steps=1000)

# Prepare session (calculates Bottleneck files)
trainer.prepare()

# Train new model and save
benchmark_info = trainer.train('./scene_type_model')

We now have a newly created model in ./scene_type_model which we can use as follows:

from transferflow.classification.runner import Runner

# Our new model
runner = Runner('./scene_type_model')
predicted_labels = runner.run('./test/fixtures/images/lake.jpg')
print(predicted_labels)

This will output the predicted labels for this image ordered by score.

First, your training data needs to be formatted like a Standard Scaffold. See Formats for more details. In this example we'll use a pre-prepared Scaffold test/fixtures/scaffolds/parking_lots that has the following:

• 40 images (screenshots from Google Maps)
• 37 annotated bounding boxes for the parking lots found in those satellite images

We'll use this to train a parking lot detector.

from transferflow.object_detection.trainer import Trainer

# Instantiate trainer with training data and configuration
scaffold_path = './test/fixtures/scaffolds/parking_lots'
base_model_path = './models/inception_v1'
trainer = Trainer(base_model_path, scaffold_path, num_steps=1000)

# Prepare session (splits up test/train data)
trainer.prepare()

# Train new model and save
trainer.train('/parking_lots')

Now our object detection model will be stored in ./parking_lots. It can be used like so:

from transferflow.object_detection.runner import Runner
from transferflow.utils import draw_rectangles

# Our new model
runner = Runner('./parking_lots')

# Run, gets back predicted bounding boxes and a resized image
resized_img, rects, raw_rects = runner.run('./test/fixtures/images/parking_lots/1.png')

# Draw all predicted bounding box rectangles on the resized image and store
new_img = draw_rectangles(resized_img, raw_rects, color=(255, 0, 0))
new_img = draw_rectangles(new_img, rects, color=(0, 255, 0))
misc.imsave('./parking_lots_validation.png', new_image)

make test

• Classification: Refactor bottleneck creation phase
• Classification: Support and benchmark different base models
• Object Detection: Make hungarian and stitch examples into proper Python modules so they jive well with Pip
• Object Detection: Clean up settings
• Object Detection: Slim down size of detection models (lots of unused nodes in there)
• Object Detection: Improve naming
• Object Detection: Refactor TensorBox originated code
• Make threads shut down gracefully