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Food image prediction using TensorFlow and calorie estimation using K-Nearest-Neighbors algoritm
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NYU CS 6293: Machine-Learning-Project


Python 3.6
TensorFlow 1.13
OpnCV 3.4
Numpy 1.12
Pandas 0.22
Docker (latest should work)
Requests 2.1

Stpes to execute our code:

  1. Install Docker

  2. docker run hello-world

  3. docker run -it

  4. Check Tensorflow:

    python import tensorflow

  5. Retrieve Images: $ ctrl-D if you're still in Docker and then: $ cd $HOME $ mkdir tf_files $ cd tf_files $ curl -O $ gzip -d food_datasets.tar.gz $ tar xzf DataSets.tar $ cd $HOME/tf_files/DataSets/food_photos

  6. Link image dataset virtually to tensorflow: $docker run -it -v /DataSets/food_photos:/tf_files/ImageDataSets/food_photos $ ls /tf_files/DataSets food_photos

  7. Retrieving the Training code: $ cd /tensorflow $ git pull

  8. Training the Inception model: $ python tensorflow/examples/image_retraining/
    -- bottleneck_dir=/tf_files/ImageDataSets/bottlenecks
    -- how_many_training_steps 500
    -- model_dir=/tf_files/ImageDataSets/inception
    -- output_graph=/tf_files/ImageDataSets/retrained_graph.pb
    -- output_labels=/tf_files/ImageDataSets/retrained_labels.txt
    -- image_dir /tf_files/DataSets/food_photos

    The retraining script will write out a version of the Inception v3 network with a final layer retrained to your categories to /tmp/output_graph.pb and a text file containing the labels to /tmp/output_labels.txt.

  9. Using trained model to predict new images: $ ctrl-D to exit Docker and then: $ curl -L > $HOME/tf_files/ImageDataSets/ $ docker run -it -v /DataSets/test_photos:/tf_files/ImageDataSets/test_photos

  10. Predicting new images: $ python /tf_files/ImageDataSets/ /ImageDataSets/test_photos/Pizza/pizza1.jpg $ python /tf_files/ /ImageDataSets/test_photos/VegBurger/notburger_cake1.jpg

Team members:

  • Harshit Pareek (hp1014),
  • Jubin Soni (jas1464),
  • Ankur Patil (asp549) GitHubIDs:@jubins @hp1014 @ankpatil18

Please contact anyone of us if you face any difficulty in executing the code.

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