├── .gitattributes ├── .gitignore ├── LICENSE.md ├── README.md ├── functions.py ├── metadata ├── animals.json ├── architecture.json ├── babies.json ├── cars.json ├── fitness.json ├── food.json ├── nature.json ├── selfie.json ├── travel.json └── wedding.json ├── modeling.ipynb ├── presentation.pdf ├── scraping.ipynb └── test ├── animals.jpg ├── architecture.jpg ├── babies.jpg ├── cars.jpg ├── fitness.jpg ├── food.jpg ├── nature.jpg ├── selfie.jpg ├── travel.jpg └── wedding.jpg /.gitattributes: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/.gitattributes -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | # This project currently (08/30/19) uses .pkl files to 6 | # re-use some important dataframes in different notebooks. 7 | # *.pkl 8 | 9 | # C extensions 10 | *.so 11 | 12 | # Distribution / packaging 13 | .Python 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | wheels/ 26 | *.egg-info/ 27 | .installed.cfg 28 | *.egg 29 | MANIFEST 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | 53 | # Translations 54 | *.mo 55 | *.pot 56 | 57 | # Django stuff: 58 | *.log 59 | local_settings.py 60 | db.sqlite3 61 | 62 | # Flask stuff: 63 | instance/ 64 | .webassets-cache 65 | 66 | # Scrapy stuff: 67 | .scrapy 68 | 69 | # Sphinx documentation 70 | docs/_build/ 71 | 72 | # PyBuilder 73 | target/ 74 | 75 | # Jupyter Notebook 76 | .ipynb_checkpoints 77 | 78 | # pyenv 79 | .python-version 80 | 81 | # celery beat schedule file 82 | celerybeat-schedule 83 | 84 | # SageMath parsed files 85 | *.sage.py 86 | 87 | # Environments 88 | .env 89 | .venv 90 | env/ 91 | venv/ 92 | ENV/ 93 | env.bak/ 94 | venv.bak/ 95 | 96 | # Spyder project settings 97 | .spyderproject 98 | .spyproject 99 | 100 | # Rope project settings 101 | .ropeproject 102 | 103 | # mkdocs documentation 104 | /site 105 | 106 | # mypy 107 | .mypy_cache/ 108 | 109 | # The data folder is for holding the image dataset of 110 | # this project. Due to GitHub's file size constraints, 111 | # it is ommitted from the repository. If you want to 112 | # recreate this work, please use the web scraping 113 | # utility: scraping.ipynb 114 | data/ 115 | 116 | # The metadata folder is for holding metadata about the 117 | # image dataset. While it easily fits GitHub's file size 118 | # constraints, it would be useless unless you had the 119 | # exact same images. The scraping.ipynb utility will 120 | # automatically produce and fill this as well. 121 | metadata/ -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) [2019] [Alec Morgan, Andy Luc, Anna Zubova] 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Instagram hashtags recommender 2 | 3 | We created a model that recommends hashtags for Instagram images within 10 categories: travel, food, animals, cars, babies, wedding, architecture, selfie, fitness, nature. 4 | 5 | Our recommendation algorithm is based on ALS model combined with graphic data extracted using transfer learning and base pretrained model MobileNetV2. 6 | 7 | **Goal**: recommend 10 Instagram hashtags for a given image. 8 | 9 | **Presentation**: [link to presentation](https://docs.google.com/presentation/d/1J3S1q_XvEhRQ1oK63WoLEthnBDFeddjv_a6V3gavuTU/edit) 10 | 11 | **Team**: [Alec Morgan](https://github.com/AlecMorgan), [Andy Luc](https://github.com/rokaandy), [Anna Zubova](https://github.com/AnnaLara) 12 | 13 | **Main working notebook**: index.ipynb 14 | 15 | **Scraping notebook**: scraping.ipynb 16 | 17 | **Ready-to-use model**: predict\*hashtag\*for\*new_image.ipynb 18 | Note that pickle files used in this notebook are not uploaded to GitHub due to the size. Please, request them if necessary. 19 | The model will be deployed as a Flask app shortly. 20 | 21 | ## Overvew 22 | 23 | ### Data collection 24 | 25 | We scraped 3000 Instagram images that had the following hashtags (300 images per hashtag): #travel, #food, #animals, #cars, #babies, #wedding, #architecture, #selfie, #fitness, #nature. We also scraped all hashtags that appeared in the comments. 26 | 27 | We initially stored the data in S3 bucket, but we experienced unexpected S3 bugs that prevented us from sharing the data between AWS accounts. As the images had an average size of just 100KB, we opted to store those locally. 28 | 29 | ### Modeling 30 | 31 | We used a **neural network** to extract deep features from the base model MobileNetV2. We then applied **transfer learning** technique and applied the the model to our data, extracting deep features from each individual image. We stored information about deep features in a separate file. 32 | 33 | Independently of the graphic data, we applied **ALS model** to the hashtag text data. This model analized relationship between hashtags and extracted model's _user features_ (which in out case were image features) and _item features_ (in our case - hashtag features). The dot product of each combination represents a kind of recommendation score that allows to select n numbers of the hashtags to recommend. The example of the recommendation made with the ALS model can be found in the presentation. The ALS model is saved in a separate file to be able to use it easily. 34 | 35 | ### Recommendation algorithm 36 | 37 | Our recommendation algorithm combines two 2 models mentioned above to reach higher accuracy of recommendations. 38 | 39 | The algorithm:: 40 | 41 | 1. Given a new image to make recommendations for, extract its deep features uning the model pretrained with MobileNetV2. 42 | 2. Find _n_ most similar images using cosine similarity between deep features. 43 | 3. For each of the _n_ most similar images find the average of the ALS's _image features_. 44 | 4. Find the dot products of the above value and each hashtag's _item features_ (from ALS) 45 | 5. Select n hashtags to recomment that have the highest dot product. 46 | 47 | ### Recommendation example 48 | 49 | Test image: 50 | ![test image](https://github.com/AnnaLara/mod_4_project/raw/master/test_wedding.jpg) 51 | 52 | Hashtags recommended by the model: 53 | 54 | 1. #love 55 | 2. #selfie 56 | 3. #fashion 57 | 4. #instagood 58 | 5. #picoftheday 59 | 6. #photography 60 | 7. #summer 61 | 8. #happy 62 | 9. #instagram 63 | 10. #beautiful 64 | 65 | ### Further steps 66 | 67 | 1. The code needs some cleaning and organizing, which will be done in the nearest future 68 | 2. The model will be deployed as a Flask app 69 | -------------------------------------------------------------------------------- /functions.py: -------------------------------------------------------------------------------- 1 | # Base of scraping code taken from owner John Naujoks with permission. 2 | # See John's scraping code (which this project expanded upon) here: 3 | # https://github.com/jnawjux/web_scraping_corgis/blob/master/insta_scrape.py 4 | 5 | import numpy as np 6 | import time 7 | import re 8 | import os 9 | import json 10 | from random import random 11 | from selenium.webdriver import Chrome, Firefox 12 | from urllib.request import urlretrieve 13 | from uuid import uuid4 14 | import boto3 15 | from io import BytesIO 16 | from PIL import Image 17 | import tensorflow as tf 18 | 19 | 20 | def get_posts(hashtag, n, browser): 21 | """With the input of an account page, scrape the n most recent posts urls""" 22 | url = f"https://www.instagram.com/explore/tags/{hashtag}/" 23 | browser.get(url) 24 | post = "https://www.instagram.com/p/" 25 | post_links = [] 26 | images = [] 27 | while len(post_links) < n or len(images) < n: 28 | 29 | img_src = [ 30 | img.get_attribute("src") 31 | for img in browser.find_elements_by_css_selector("article img") 32 | ] 33 | links = [ 34 | a.get_attribute("href") for a in browser.find_elements_by_tag_name("a") 35 | ] 36 | 37 | for link in links: 38 | if post in link and link not in post_links and len(post_links) < n: 39 | post_links.append(link) 40 | 41 | for image in img_src: 42 | if image not in images and len(images) < n: 43 | images.append(image) 44 | 45 | scroll_down = "window.scrollTo(0, document.body.scrollHeight);" 46 | browser.execute_script(scroll_down) 47 | time.sleep(1 + (random() * 5)) 48 | 49 | return [ 50 | {"post_link": post_links[i], "image": images[i], "search_hashtag": hashtag} 51 | for i in range(len(post_links)) 52 | ] 53 | 54 | 55 | def get_hashtags(url, browser): 56 | """Return a list of hashtags found in all post's comments""" 57 | browser.get(url) 58 | comments_html = browser.find_elements_by_css_selector("span") 59 | all_hashtags = [] 60 | 61 | for comment in comments_html: 62 | hashtags = re.findall("#[A-Za-z]+", comment.text) 63 | if len(hashtags) > 0: 64 | all_hashtags.extend(hashtags) 65 | return list(set(all_hashtags)) 66 | 67 | 68 | def get_image(url, hashtag): 69 | """Download image from given url and return it's name""" 70 | uuid = uuid4() 71 | urlretrieve(url, f"data/{hashtag}/{uuid}.jpg") 72 | name = f"{uuid}.jpg" 73 | return name 74 | 75 | 76 | def scrape_data(hashtags, n, delay=5): 77 | """ 78 | Download n images and return a dictionary with their metadata. 79 | """ 80 | browser = Firefox() 81 | 82 | for hashtag in hashtags: 83 | 84 | posts = get_posts(hashtag, n, browser) 85 | 86 | try: 87 | os.mkdir(f"data/{hashtag}") 88 | except OSError: 89 | pass # We probably tried to make something that already exists 90 | 91 | try: 92 | for post in posts: 93 | post["hashtags"] = get_hashtags(post["post_link"], browser) 94 | time.sleep(random() * delay) 95 | post["image_local_name"] = get_image(post["image"], hashtag) 96 | time.sleep(random() * delay) 97 | new_hashtag_metadata = posts 98 | except: 99 | new_hashtag_metadata = posts 100 | 101 | 102 | #NOTE TO SELF: transferred code begins here 103 | if os.path.exists(f"metadata/{hashtag}.json"): 104 | # We already have metadata for this hashtag, add to it 105 | with open(f"metadata/{hashtag}.json", "r") as f: 106 | hashtag_metadata = json.load(f) 107 | hashtag_metadata += new_hashtag_metadata 108 | else: 109 | # We don't have metadata for this hashtag yet, initialize it 110 | hashtag_metadata = new_hashtag_metadata 111 | 112 | with open(f"metadata/{hashtag}.json", "w") as f: 113 | json.dump(hashtag_metadata, f) 114 | 115 | 116 | def prepare_image(img_path, height=160, width=160, where='s3'): 117 | """Downsample and scale image to prepare it for neural network""" 118 | if where=='s3': 119 | img = fetch_image_from_s3_to_array('instagram-images-mod4', img_path) 120 | elif where == 'local': 121 | # If the image is stored locally: 122 | img = tf.io.read_file(img_path) 123 | img = tf.image.decode_image(img) 124 | img = tf.cast(img, tf.float32) 125 | img = (img/127.5) - 1 126 | img = tf.image.resize(img, (height, width)) 127 | # Reshape grayscale images to match dimensions of color images 128 | if img.shape != (160, 160, 3): 129 | img = tf.concat([img, img, img], axis=2) 130 | return img 131 | 132 | 133 | def extract_features(image, neural_network): 134 | """Return a vector of 1280 deep features for image.""" 135 | image_np = image.numpy() 136 | images_np = np.expand_dims(image_np, axis=0) 137 | deep_features = neural_network.predict(images_np)[0] 138 | return deep_features 139 | 140 | 141 | def upload_files_to_s3( 142 | dir_path, hashtag, bucket_name 143 | ): #ex. dir_path: 'data/cars/' ; hashtag: 'travel' 144 | """Upload files from the folder to S3 bucket name: 'instagram-images-mod4' to a seperate folder with hashtag name, 145 | and make a list of images filenames stored in local directory""" 146 | s3 = boto3.resource("s3") 147 | f = [] 148 | for (dirpath, dirnames, filenames) in walk(dir_path): 149 | f.extend(filenames) 150 | break 151 | 152 | for name in f: 153 | source = dir_path + name 154 | bucket = bucket_name 155 | destination = hashtag + "/" + name 156 | s3.meta.client.upload_file(source, bucket, destination) 157 | 158 | 159 | def fetch_image_from_s3_to_array(bucket, key): 160 | """Fetches an image from S3 and returns a numpy array.""" 161 | s3 = boto3.client("s3") 162 | response = s3.get_object(Bucket=bucket, Key=key) 163 | body = response["Body"] 164 | data = body.read() 165 | f = BytesIO(data) 166 | image = Image.open(f) 167 | image_data = np.asarray(image) 168 | return image_data 169 | 170 | 171 | def fetch_image_from_s3(bucket, key): 172 | """Fetches an image from S3""" 173 | s3 = boto3.client("s3") 174 | response = s3.get_object(Bucket=bucket, Key=key) 175 | body = response["Body"] 176 | data = body.read() 177 | f = BytesIO(data) 178 | image = Image.open(f) 179 | return image -------------------------------------------------------------------------------- /metadata/architecture.json: -------------------------------------------------------------------------------- 1 | [{"hashtags": ["architecture"], "image": null, "image_local_name": "8f84b0b1-5b40-4ecf-9b81-8d809cbd1d93.jpg", "post_link": null, "search_hashtag": "architecture"}, {"hashtags": ["architecture"], "image": null, "image_local_name": "460d80f8-2969-4d47-9e53-cb47ecc1aa38.jpg", "post_link": null, "search_hashtag": "architecture"}, {"hashtags": ["architecture"], "image": null, "image_local_name": "61c1b2ba-cf88-4fdc-be6a-a92c61ae19b9.jpg", "post_link": null, "search_hashtag": "architecture"}, {"hashtags": ["architecture"], "image": null, "image_local_name": "8298003f-54eb-44df-a284-c4fa7eff582a.jpg", "post_link": null, "search_hashtag": 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-------------------------------------------------------------------------------- /scraping.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Scraping\n", 8 | "\n", 9 | "### Using Selenium, this notebook initializes a zombie web browser to scrape images from Instagram's latest posts. First it navigates to a hashtag's page and grabs links to a certain number of posts, then it visits each post and grabs its image, its hashtags, and a few other pieces of information such as a direct link to the post. All of this data is then saved directly into the `data` and `metadata` folders. \n", 10 | "\n", 11 | "### " 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# NumPy versions below 1.17 may be incompatible with some other \n", 21 | "# packages, so you may need to replace your current version with \n", 22 | "# an earlier one in order to run this notebook as-is. \n", 23 | "# !pip uninstall numpy --yes\n", 24 | "# !pip install \"numpy<1.17\"" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 2, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "import numpy as np\n", 34 | "import pandas as pd\n", 35 | "# from selenium.webdriver import Chrome, Firefox\n", 36 | "from functions import scrape_data\n", 37 | "import os" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "### **If you want to scrape your own hashtags,** simply add them to the list, choose how many you want to be scraped, and run the remaining cells. " 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "# EXAMPLE:\n", 54 | "# hashtags = [\"travel\", \"food\", \"animals\", \"selfie\", \"cars\", \"fitness\", \"babies\", \"wedding\", \"nature\", \"architecture\"]\n", 55 | "\n", 56 | "# Your own hashtags here:\n", 57 | "hashtags = []\n", 58 | "\n", 59 | "# How many hashtags to scrape:\n", 60 | "num_to_scrape = 0" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 4, 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "Folder 'data' already exists.\n", 73 | "Folder 'metadata' already exists.\n" 74 | ] 75 | } 76 | ], 77 | "source": [ 78 | "# Make sure our data and metadata folders exist before we start scraping\n", 79 | "folder_names = [\"data\", \"metadata\"]\n", 80 | "for folder_name in folder_names:\n", 81 | " try:\n", 82 | " os.mkdir(folder_name)\n", 83 | " except OSError:\n", 84 | " print(f\"Folder '{folder_name}' already exists.\")" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 5, 90 | "metadata": {}, 91 | "outputs": [], 92 | "source": [ 93 | "# \"delay\" is how long to wait between grabbing each image, to avoid being \n", 94 | "# blocked by Instagram. If delay=5 for example, then the browser will \n", 95 | "# randomly wait between 0 to 5 seconds before grabbing each new image.\n", 96 | "scrape_data(hashtags, num_to_scrape, delay=5)" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "### You can use `pd.read_json` to import hashtag data again. " 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "# travel_df = pd.read_json(\"metadata/travel.json\")\n", 113 | "# travel_df.head()" 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": {}, 119 | "source": [ 120 | "### Optionally you can also use this scaffolding for uploading scraped images to an S3 bucket, although you will of course need to set up your own S3 bucket." 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "# import boto3\n", 130 | "\n", 131 | "# s3 = boto3.resource(\"s3\")\n", 132 | "\n", 133 | "# hashtags_to_upload = [\"foo\", \"bar\"]\n", 134 | "# for hashtag in hashtags_to_upload:\n", 135 | "# for img in hashtag: \n", 136 | "# source = f\"data/{img[\"image_local_name\"]}\"\n", 137 | "# bucket = f\"instagram-images-mod4\"\n", 138 | "# destination = f\"{img[\"search_hashtag\"]}/{img[\"image_local_name\"]}\"\n", 139 | "# s3.meta.client.upload_file(source, bucket, destination)" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [] 148 | } 149 | ], 150 | "metadata": { 151 | "kernelspec": { 152 | "display_name": "Python 3", 153 | "language": "python", 154 | "name": "python3" 155 | }, 156 | "language_info": { 157 | "codemirror_mode": { 158 | "name": "ipython", 159 | "version": 3 160 | }, 161 | "file_extension": ".py", 162 | "mimetype": "text/x-python", 163 | "name": "python", 164 | "nbconvert_exporter": "python", 165 | "pygments_lexer": "ipython3", 166 | "version": "3.7.1" 167 | } 168 | }, 169 | "nbformat": 4, 170 | "nbformat_minor": 2 171 | } 172 | -------------------------------------------------------------------------------- /test/animals.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/animals.jpg -------------------------------------------------------------------------------- /test/architecture.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/architecture.jpg -------------------------------------------------------------------------------- /test/babies.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/babies.jpg -------------------------------------------------------------------------------- /test/cars.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/cars.jpg -------------------------------------------------------------------------------- /test/fitness.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/fitness.jpg -------------------------------------------------------------------------------- /test/food.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/food.jpg -------------------------------------------------------------------------------- /test/nature.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/nature.jpg -------------------------------------------------------------------------------- /test/selfie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/selfie.jpg -------------------------------------------------------------------------------- /test/travel.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/travel.jpg -------------------------------------------------------------------------------- /test/wedding.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlecMorgan/Automatic-Image-Tagger/b94960215fe78e6fc7fb2229a73fecc7d12ae98b/test/wedding.jpg --------------------------------------------------------------------------------