{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Building an image retrieval system with deep features\n", "\n", "\n", "#Fire up GraphLab Create" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A newer version of GraphLab Create (v1.7.1) is available! Your current version is v1.6.1.\n", "\n", "You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n" ] } ], "source": [ "import graphlab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Load the CIFAR-10 dataset\n", "\n", "We will use a popular benchmark dataset in computer vision called CIFAR-10. \n", "\n", "(We've reduced the data to just 4 categories = {'cat','bird','automobile','dog'}.)\n", "\n", "This dataset is already split into a training set and test set. In this simple retrieval example, there is no notion of \"testing\", so we will only use the training data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] This non-commercial license of GraphLab Create is assigned to danielbenitezr@yahoo.com and will expire on September 27, 2016. For commercial licensing options, visit https://dato.com/buy/.\n", "\n", "[INFO] Start server at: ipc:///tmp/graphlab_server-7627 - Server binary: /home/balrog/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1450965281.log\n", "[INFO] GraphLab Server Version: 1.6.1\n" ] } ], "source": [ "image_train = graphlab.SFrame('image_train_data/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Computing deep features for our images\n", "\n", "The two lines below allow us to compute deep features. This computation takes a little while, so we have already computed them and saved the results as a column in the data you loaded. \n", "\n", "(Note that if you would like to compute such deep features and have a GPU on your machine, you should use the GPU enabled GraphLab Create, which will be significantly faster for this task.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#deep_learning_model = graphlab.load_model('http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45')\n", "#image_train['deep_features'] = deep_learning_model.extract_features(image_train)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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idimagelabeldeep_featuresimage_array
24Height: 32 Width: 32bird[0.242871761322,
1.09545373917, 0.0, ...		[73.0, 77.0, 58.0, 71.0,
68.0, 50.0, 77.0, 69.0, ...
33Height: 32 Width: 32cat[0.525087952614, 0.0,
0.0, 0.0, 0.0, 0.0, ...		[7.0, 5.0, 8.0, 7.0, 5.0,
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36Height: 32 Width: 32cat[0.566015958786, 0.0,
0.0, 0.0, 0.0, 0.0, ...		[169.0, 122.0, 65.0,
131.0, 108.0, 75.0, ...
70Height: 32 Width: 32dog[1.12979578972, 0.0, 0.0,
0.778194487095, 0.0, ...		[154.0, 179.0, 152.0,
159.0, 183.0, 157.0, ...
90Height: 32 Width: 32bird[1.71786928177, 0.0, 0.0,
0.0, 0.0, 0.0, ...		[216.0, 195.0, 180.0,
201.0, 178.0, 160.0, ...
97Height: 32 Width: 32automobile[1.57818555832, 0.0, 0.0,
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58.0, 38.0, 107.0, 61.0, ...
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56.0, 56.0, 42.0, 62.0, ...
138Height: 32 Width: 32bird[0.658935725689, 0.0,
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\n", "[10 rows x 5 columns]
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" ], "text/plain": [ "Columns:\n", "\tid\tint\n", "\timage\tImage\n", "\tlabel\tstr\n", "\tdeep_features\tarray\n", "\timage_array\tarray\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-----+----------------------+------------+-------------------------------+\n", "| id | image | label | deep_features |\n", "+-----+----------------------+------------+-------------------------------+\n", "| 24 | Height: 32 Width: 32 | bird | [0.242871761322, 1.0954537... |\n", "| 33 | Height: 32 Width: 32 | cat | [0.525087952614, 0.0, 0.0,... |\n", "| 36 | Height: 32 Width: 32 | cat | [0.566015958786, 0.0, 0.0,... |\n", "| 70 | Height: 32 Width: 32 | dog | [1.12979578972, 0.0, 0.0, ... |\n", "| 90 | Height: 32 Width: 32 | bird | [1.71786928177, 0.0, 0.0, ... |\n", "| 97 | Height: 32 Width: 32 | automobile | [1.57818555832, 0.0, 0.0, ... |\n", "| 107 | Height: 32 Width: 32 | dog | [0.0, 0.0, 0.220677852631,... |\n", "| 121 | Height: 32 Width: 32 | bird | [0.0, 0.23753464222, 0.0, ... |\n", "| 136 | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n", "| 138 | Height: 32 Width: 32 | bird | [0.658935725689, 0.0, 0.0,... |\n", "+-----+----------------------+------------+-------------------------------+\n", "+-------------------------------+\n", "| image_array |\n", "+-------------------------------+\n", "| [73.0, 77.0, 58.0, 71.0, 6... |\n", "| [7.0, 5.0, 8.0, 7.0, 5.0, ... |\n", "| [169.0, 122.0, 65.0, 131.0... |\n", "| [154.0, 179.0, 152.0, 159.... |\n", "| [216.0, 195.0, 180.0, 201.... |\n", "| [33.0, 44.0, 27.0, 29.0, 4... |\n", "| [97.0, 51.0, 31.0, 104.0, ... |\n", "| [93.0, 96.0, 88.0, 102.0, ... |\n", "| [35.0, 59.0, 53.0, 36.0, 5... |\n", "| [205.0, 193.0, 195.0, 200.... |\n", "+-------------------------------+\n", "[10 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Train a nearest-neighbors model for retrieving images using deep features\n", "\n", "We will now build a simple image retrieval system that finds the nearest neighbors for any image." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Starting brute force nearest neighbors model training.\n" ] } ], "source": [ "knn_model = graphlab.nearest_neighbors.create(image_train,features=['deep_features'],\n", " label='id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Use image retrieval model with deep features to find similar images\n", "\n", "Let's find similar images to this cat picture." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", 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get_images_from_ids(query_result):\n", " return image_train.filter_by(query_result['reference_label'],'id')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Starting pairwise querying.\n", "PROGRESS: +--------------+---------+-------------+--------------+\n", "PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |\n", "PROGRESS: +--------------+---------+-------------+--------------+\n", "PROGRESS: | 0 | 1 | 0.0498753 | 37.219ms |\n", "PROGRESS: | Done | | 100 | 397.196ms |\n", "PROGRESS: +--------------+---------+-------------+--------------+\n" ] } ], "source": [ "cat_neighbors = get_images_from_ids(knn_model.query(cat))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: 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"outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Starting pairwise querying.\n", "PROGRESS: +--------------+---------+-------------+--------------+\n", "PROGRESS: | Query points | # Pairs | % Complete. | Elapsed Time |\n", "PROGRESS: +--------------+---------+-------------+--------------+\n", "PROGRESS: | 0 | 1 | 0.0498753 | 27.126ms |\n", "PROGRESS: | Done | | 100 | 406.319ms |\n", "PROGRESS: +--------------+---------+-------------+--------------+\n" ] }, { "data": { "application/javascript": [ "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//dato.com/files/canvas/1.6.1/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = 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