initial commit

SessionState.py

@@ -0,0 +1,117 @@
+"""Hack to add per-session state to Streamlit.
+
+Usage
+-----
+
+>>> import SessionState
+>>>
+>>> session_state = SessionState.get(user_name='', favorite_color='black')
+>>> session_state.user_name
+''
+>>> session_state.user_name = 'Mary'
+>>> session_state.favorite_color
+'black'
+
+Since you set user_name above, next time your script runs this will be the
+result:
+>>> session_state = get(user_name='', favorite_color='black')
+>>> session_state.user_name
+'Mary'
+
+"""
+try:
+    import streamlit.ReportThread as ReportThread
+    from streamlit.server.Server import Server
+except Exception:
+    # Streamlit >= 0.65.0
+    import streamlit.report_thread as ReportThread
+    from streamlit.server.server import Server
+
+
+class SessionState(object):
+    def __init__(self, **kwargs):
+        """A new SessionState object.
+
+        Parameters
+        ----------
+        **kwargs : any
+            Default values for the session state.
+
+        Example
+        -------
+        >>> session_state = SessionState(user_name='', favorite_color='black')
+        >>> session_state.user_name = 'Mary'
+        ''
+        >>> session_state.favorite_color
+        'black'
+
+        """
+        for key, val in kwargs.items():
+            setattr(self, key, val)
+
+
+def get(**kwargs):
+    """Gets a SessionState object for the current session.
+
+    Creates a new object if necessary.
+
+    Parameters
+    ----------
+    **kwargs : any
+        Default values you want to add to the session state, if we're creating a
+        new one.
+
+    Example
+    -------
+    >>> session_state = get(user_name='', favorite_color='black')
+    >>> session_state.user_name
+    ''
+    >>> session_state.user_name = 'Mary'
+    >>> session_state.favorite_color
+    'black'
+
+    Since you set user_name above, next time your script runs this will be the
+    result:
+    >>> session_state = get(user_name='', favorite_color='black')
+    >>> session_state.user_name
+    'Mary'
+
+    """
+    # Hack to get the session object from Streamlit.
+
+    ctx = ReportThread.get_report_ctx()
+
+    this_session = None
+
+    current_server = Server.get_current()
+    if hasattr(current_server, '_session_infos'):
+        # Streamlit < 0.56
+        session_infos = Server.get_current()._session_infos.values()
+    else:
+        session_infos = Server.get_current()._session_info_by_id.values()
+
+    for session_info in session_infos:
+        s = session_info.session
+        if (
+            # Streamlit < 0.54.0
+            (hasattr(s, '_main_dg') and s._main_dg == ctx.main_dg)
+            or
+            # Streamlit >= 0.54.0
+            (not hasattr(s, '_main_dg') and s.enqueue == ctx.enqueue)
+            or
+            # Streamlit >= 0.65.2
+            (not hasattr(s, '_main_dg') and s._uploaded_file_mgr == ctx.uploaded_file_mgr)
+        ):
+            this_session = s
+
+    if this_session is None:
+        raise RuntimeError(
+            "Oh noes. Couldn't get your Streamlit Session object. "
+            'Are you doing something fancy with threads?')
+
+    # Got the session object! Now let's attach some state into it.
+
+    if not hasattr(this_session, '_custom_session_state'):
+        this_session._custom_session_state = SessionState(**kwargs)
+
+    return this_session._custom_session_state

app.py

@@ -0,0 +1,367 @@
+import json
+import os
+import pickle
+import random
+import time
+from collections import Counter
+from datetime import datetime
+from glob import glob
+
+import gdown
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import streamlit as st
+from PIL import Image
+
+import SessionState
+from download_utils import *
+from image_utils import *
+
+random.seed(datetime.now())
+np.random.seed(int(time.time()))
+
+NUMBER_OF_TRIALS = 20
+CLASSIFIER_TAG = "CHM"
+explaination_functions = [load_chm_nns, load_knn_nns]
+selected_xai_tool = None
+
+# Config
+folder_to_name = {}
+class_descriptions = {}
+classifier_predictions = {}
+selected_dataset = "Final"
+
+root_visualization_dir = "./visualizations/"
+viz_url = "https://static.taesiri.com/xai/Final.zip"
+viz_archivefile = "Final.zip"
+
+demonstration_url = "https://static.taesiri.com/xai/demonstrations.zip"
+demonst_zipfile = "demonstrations.zip"
+
+picklefile_url = "https://static.taesiri.com/xai/Task1_Results_CHM_and_EMD.pickle"
+prediction_root = "./predictions/"
+prediction_pickle = f"{prediction_root}predictions.pickle"
+
+################################################
+# GLOBAL VARIABLES
+app_mode = ""
+
+## Shared/Global Information
+with open("imagenet-labels.json", "rb") as f:
+    folder_to_name = json.load(f)
+
+with open("gloss.txt", "r") as f:
+    description_file = f.readlines()
+
+class_descriptions = {l.split("\t")[0]: l.split("\t")[1] for l in description_file}
+################################################
+
+with open(prediction_pickle, "rb") as f:
+    classifier_predictions = pickle.load(f)
+
+# SESSION STATE
+session_state = SessionState.get(
+    page=1,
+    first_run=1,
+    user_feedback={},
+    queries=[],
+    is_classifier_correct={},
+    XAI_tool="Unselected",
+)
+################################################
+
+
+def get_data():
+    download_files(
+        root_visualization_dir,
+        viz_url,
+        viz_archivefile,
+        demonstration_url,
+        demonst_zipfile,
+        picklefile_url,
+        prediction_root,
+        prediction_pickle,
+    )
+
+
+def resmaple_queries():
+    if session_state.first_run == 1:
+        both_correct = glob(
+            root_visualization_dir + selected_dataset + "/Both_correct/*.JPEG"
+        )
+        both_wrong = glob(
+            root_visualization_dir + selected_dataset + "/Both_wrong/*.JPEG"
+        )
+
+        correct_samples = list(
+            np.random.choice(a=both_correct, size=NUMBER_OF_TRIALS // 2, replace=False)
+        )
+        wrong_samples = list(
+            np.random.choice(a=both_wrong, size=NUMBER_OF_TRIALS // 2, replace=False)
+        )
+
+        all_images = correct_samples + wrong_samples
+        random.shuffle(all_images)
+        session_state.queries = all_images
+        session_state.first_run = -1
+        # RESET INTERACTIONS
+        session_state.user_feedback = {}
+        session_state.is_classifier_correct = {}
+
+
+def render_experiment(query):
+    current_query = session_state.queries[query]
+    query_id = os.path.basename(current_query)
+
+    predicted_wnid = classifier_predictions[query_id][f"{CLASSIFIER_TAG}-predictions"]
+    prediction_confidence = classifier_predictions[query_id][
+        f"{CLASSIFIER_TAG}-confidence"
+    ]
+    prediction_label = folder_to_name[predicted_wnid]
+    class_def = class_descriptions[predicted_wnid]
+
+    session_state.is_classifier_correct[query_id] = classifier_predictions[query_id][
+        f"{CLASSIFIER_TAG}-Output"
+    ]
+
+    ################################### SHOW DESCRIPTION OF CLASS
+    with st.expander("Show Class Description"):
+        st.write(f"**Name**: {prediction_label}")
+        st.write("**Class Definition**:")
+        st.markdown("`" + class_def + "`")
+        st.image(
+            Image.open(f"demonstrations/{predicted_wnid}.jpeg"),
+            caption=f"Class Explanation",
+            use_column_width=True,
+        )
+
+    ################################### SHOW QUERY and PREDICTION
+    with st.expander("Show Query"):
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(load_query(current_query), caption=f"Query ID: {query_id}")
+        with col2:
+            default_value = 0
+            if query_id in session_state.user_feedback.keys():
+                if session_state.user_feedback[query_id] == "Correct":
+                    default_value = 1
+                elif session_state.user_feedback[query_id] == "Wrong":
+                    default_value = 2
+
+            session_state.user_feedback[query_id] = st.radio(
+                "What do you think about model's prediction?",
+                ("-", "Correct", "Wrong"),
+                key=query_id,
+                index=default_value,
+            )
+            st.write(f"**Model Prediction**: {prediction_label}")
+            st.write(f"**Model Confidence**: {prediction_confidence}")
+
+        ################################### SHOW Model Explanation
+        if selected_xai_tool is not None:
+            st.image(
+                selected_xai_tool(current_query),
+                caption=f"Explaination",
+                use_column_width=True,
+            )
+
+    ################################### SHOW DEBUG INFO
+
+    if st.button("Debug: Show Everything"):
+        st.image(Image.open(current_query))
+
+
+def render_results():
+    user_correct_guess = 0
+    for q in session_state.user_feedback.keys():
+        if session_state.is_classifier_correct[q] == session_state.user_feedback[q]:
+            user_correct_guess += 1
+
+    st.write(
+        f"User performance on {CLASSIFIER_TAG}: {user_correct_guess} out of {len( session_state.user_feedback)} Correct"
+    )
+    st.markdown("##  User Performance Breakdown")
+
+    categories = set(session_state.is_classifier_correct.values())
+    breakdown_stats_correct = {c: 0 for c in categories}
+    breakdown_stats_wrong = {c: 0 for c in categories}
+
+    experiment_summary = []
+
+    for q in session_state.user_feedback.keys():
+        category = session_state.is_classifier_correct[q]
+        user_feedback_boolean = (
+            True if session_state.user_feedback[q] == "Correct" else False
+        )
+
+        is_user_correct = category == user_feedback_boolean
+
+        if is_user_correct:
+            breakdown_stats_correct[category] += 1
+        else:
+            breakdown_stats_wrong[category] += 1
+
+        experiment_summary.append(
+            [
+                q,
+                classifier_predictions[q]["real-gts"],
+                folder_to_name[
+                    classifier_predictions[q][f"{CLASSIFIER_TAG}-predictions"]
+                ],
+                category,
+                session_state.user_feedback[q],
+                is_user_correct,
+            ]
+        )
+
+    experiment_summary_df = pd.DataFrame.from_records(
+        experiment_summary,
+        columns=[
+            "Query",
+            "GT Labels",
+            f"{CLASSIFIER_TAG} Prediction",
+            "Category",
+            "User Prediction",
+            "Is User Prediction Correct",
+        ],
+    )
+    st.write("Summary", experiment_summary_df)
+
+    csv = convert_df(experiment_summary_df)
+    st.download_button(
+        "Press to Download", csv, "summary.csv", "text/csv", key="download-records"
+    )
+
+
+def render_menu():
+    # Render the readme as markdown using st.markdown.
+    readme_text = st.markdown(
+        """
+  # Instructions
+  ```
+  When testing this study, you should first see the class definition, then hide the expander and see the query.
+  ```
+  """
+    )
+
+    app_mode = st.selectbox(
+        "Choose the page to show:",
+        ["Experiment Instruction", "Start Experiment", "See the Results"],
+    )
+
+    if app_mode == "Experiment Instruction":
+        st.success("To continue select an option in the dropdown menu.")
+    elif app_mode == "Start Experiment":
+        # Clear Canvas
+        readme_text.empty()
+
+        page_id = session_state.page
+        col1, col4, col2, col3 = st.columns(4)
+        prev_page = col1.button("Previous Image")
+
+        if prev_page:
+            page_id -= 1
+            if page_id < 1:
+                page_id = 1
+
+        next_page = col2.button("Next Image")
+
+        if next_page:
+            page_id += 1
+            if page_id > NUMBER_OF_TRIALS:
+                page_id = NUMBER_OF_TRIALS
+
+        if page_id == NUMBER_OF_TRIALS:
+            st.success(
+                'You have reached the last image. Please go to the "Results" page to see your performance.'
+            )
+            if st.button("View"):
+                app_mode = "See the Results"
+
+        if col3.button("Resample"):
+            st.write("Restarting ...")
+            page_id = 1
+            session_state.first_run = 1
+            resmaple_queries()
+
+        session_state.page = page_id
+        st.write(f"Render Experiment: {session_state.page}")
+        render_experiment(session_state.page - 1)
+    elif app_mode == "See the Results":
+        readme_text.empty()
+        st.write("Results Summary")
+        render_results()
+
+
+def main():
+    global app_mode
+    global session_state
+    global selected_xai_tool
+    # Get the Data
+    get_data()
+
+    # Set the session state
+    # State Management and General Setup
+    st.set_page_config(layout="wide")
+    st.title("TASK - 1 - ImageNetREAL")
+
+    options = [
+        "Unselected",
+        "NOXAI",
+        "KNN",
+        "EMD Nearest Neighbors",
+        "EMD Correspondence",
+        "CHM Nearest Neighbors",
+        "CHM Correspondence",
+    ]
+
+    st.markdown(
+        """ <style>
+              div[role="radiogroup"] >  :first-child{
+                  display: none !important;
+              }
+          </style>
+          """,
+        unsafe_allow_html=True,
+    )
+
+    if session_state.XAI_tool == "Unselected":
+        default = options.index(session_state.XAI_tool)
+        session_state.XAI_tool = st.radio(
+            "What explaination tool do you want to evaluate?",
+            options,
+            key="which_xai",
+            index=default,
+        )
+        # print(session_state.XAI_tool)
+
+    if session_state.XAI_tool != "Unselected":
+        st.markdown(f"## SELECTED METHOD ``{session_state.XAI_tool}``")
+
+        if session_state.XAI_tool == "NOXAI":
+            selected_xai_tool = None
+            CLASSIFIER_TAG = "KNN"
+        elif session_state.XAI_tool == "KNN":
+            selected_xai_tool = load_knn_nns
+            CLASSIFIER_TAG = "KNN"
+        elif session_state.XAI_tool == "CHM Nearest Neighbors":
+            selected_xai_tool = load_chm_nns
+            CLASSIFIER_TAG = "CHM"
+        elif session_state.XAI_tool == "CHM Correspondence":
+            selected_xai_tool = load_chm_corrs
+            CLASSIFIER_TAG = "CHM"
+        elif session_state.XAI_tool == "EMD Nearest Neighbors":
+            selected_xai_tool = load_emd_nns
+            CLASSIFIER_TAG = "EMD"
+        elif session_state.XAI_tool == "EMD Correspondence":
+            selected_xai_tool = load_emd_corrs
+            CLASSIFIER_TAG = "EMD"
+
+        resmaple_queries()
+        render_menu()
+
+
+if __name__ == "__main__":
+    main()

download_utils.py

@@ -0,0 +1,55 @@
+import json
+import os
+import pickle
+import random
+import tarfile
+import zipfile
+from collections import Counter
+from glob import glob
+
+import gdown
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import streamlit as st
+from PIL import Image
+
+import SessionState
+
+
+def download_files(
+    root_visualization_dir,
+    viz_url,
+    viz_archivefile,
+    demonstration_url,
+    demonst_zipfile,
+    picklefile_url,
+    prediction_root,
+    prediction_pickle,
+):
+    # Get Visualization
+    if not os.path.exists(root_visualization_dir):
+        gdown.download(viz_url, viz_archivefile, quiet=False)
+        os.makedirs(root_visualization_dir, exist_ok=True)
+
+        if viz_archivefile.endswith("tar.gz"):
+            tar = tarfile.open(viz_archivefile, "r:gz")
+            tar.extractall(path=root_visualization_dir)
+            tar.close()
+        elif viz_archivefile.endswith("zip"):
+            with zipfile.ZipFile(viz_archivefile, "r") as zip_ref:
+                zip_ref.extractall(root_visualization_dir)
+
+    # Get Demonstrations
+    if not os.path.exists(demonst_zipfile):
+        gdown.download(demonstration_url, demonst_zipfile, quiet=False)
+        # os.makedirs(roo_demonstration_dir, exist_ok=True)
+
+    with zipfile.ZipFile(demonst_zipfile, "r") as zip_ref:
+        zip_ref.extractall("./")
+
+    # Get Predictions
+    if not os.path.exists(prediction_pickle):
+        os.makedirs(prediction_root, exist_ok=True)
+        gdown.download(picklefile_url, prediction_pickle, quiet=False)

helper.py

@@ -0,0 +1,23 @@
+import os
+
+def get_label_for_query(image_url, model_name):
+      fourway_label = image_url.split('/')[-2]
+  
+  if fourway_label=='both_correct':
+    return 'Correct'
+  
+  if fourway_label=='both_wrong':
+    return 'Wrong'
+  
+  if fourway_label == 'chm_correct_knn_incorrect' and model_name == 'CHM':
+    return 'Correct'
+  elif fourway_label == 'knn_correct_chm_incorrect' and model_name == 'KNN':
+   return 'Correct'
+ 
+  return 'Wrong'
+
+def get_category(image_url):
+  return image_url.split('/')[-2]
+
+def translate_winds_to_names(winds):
+  return [folder_to_name[x] for x in winds]

image_utils.py

@@ -0,0 +1,137 @@
+import json
+import os
+import pickle
+import random
+from glob import glob
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import streamlit as st
+from PIL import Image
+
+
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_query(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop(
+        (left + 75, top + 145, right - 2025, bottom - 2915)
+    ).resize((300, 300))
+
+    return cropped_image
+
+
+# CHM ############################################################################
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_chm_nns(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop((left + 475, top + 140, right - 280, bottom - 2920))
+    return cropped_image
+
+
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_chm_corrs(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop((left + 475, top + 875, right - 280, bottom - 1810))
+    return cropped_image
+
+
+# CHM ############################################################################
+
+# KNN ############################################################################
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_knn_nns(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop((left + 475, top + 510, right - 280, bottom - 2550))
+    return cropped_image
+
+
+# KNN ############################################################################
+
+# EMD ############################################################################
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_emd_nns(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop((left + 10, top + 2075, right - 420, bottom - 925))
+    return cropped_image
+
+
+@st.cache(allow_output_mutation=True, max_entries=10, ttl=3600)
+def load_emd_corrs(image_path):
+    image = Image.open(image_path)
+    width, height = image.size
+
+    new_width = width
+    new_height = height
+
+    left = (width - new_width) / 2
+    top = (height - new_height) / 2
+    right = (width + new_width) / 2
+    bottom = (height + new_height) / 2
+
+    # Crop the center of the image
+    cropped_image = image.crop((left + 10, top + 2500, right - 20, bottom))
+    return cropped_image
+
+
+# EMD ############################################################################
+
+
+@st.cache()
+def convert_df(df):
+    return df.to_csv().encode("utf-8")

imagenet-labels.json

@@ -0,0 +1,1002 @@
+{
+    "n01440764" :	"tench",
+    "n01443537" :	"goldfish",
+    "n01484850" :	"great_white_shark",
+    "n01491361" :	"tiger_shark",
+    "n01494475" :	"hammerhead",
+    "n01496331" :	"electric_ray",
+    "n01498041" :	"stingray",
+    "n01514668" :	"cock",
+    "n01514859" :	"hen",
+    "n01518878" :	"ostrich",
+    "n01530575" :	"brambling",
+    "n01531178" :	"goldfinch",
+    "n01532829" :	"house_finch",
+    "n01534433" :	"junco",
+    "n01537544" :	"indigo_bunting",
+    "n01558993" :	"robin",
+    "n01560419" :	"bulbul",
+    "n01580077" :	"jay",
+    "n01582220" :	"magpie",
+    "n01592084" :	"chickadee",
+    "n01601694" :	"water_ouzel",
+    "n01608432" :	"kite",
+    "n01614925" :	"bald_eagle",
+    "n01616318" :	"vulture",
+    "n01622779" :	"great_grey_owl",
+    "n01629819" :	"European_fire_salamander",
+    "n01630670" :	"common_newt",
+    "n01631663" :	"eft",
+    "n01632458" :	"spotted_salamander",
+    "n01632777" :	"axolotl",
+    "n01641577" :	"bullfrog",
+    "n01644373" :	"tree_frog",
+    "n01644900" :	"tailed_frog",
+    "n01664065" :	"loggerhead",
+    "n01665541" :	"leatherback_turtle",
+    "n01667114" :	"mud_turtle",
+    "n01667778" :	"terrapin",
+    "n01669191" :	"box_turtle",
+    "n01675722" :	"banded_gecko",
+    "n01677366" :	"common_iguana",
+    "n01682714" :	"American_chameleon",
+    "n01685808" :	"whiptail",
+    "n01687978" :	"agama",
+    "n01688243" :	"frilled_lizard",
+    "n01689811" :	"alligator_lizard",
+    "n01692333" :	"Gila_monster",
+    "n01693334" :	"green_lizard",
+    "n01694178" :	"African_chameleon",
+    "n01695060" :	"Komodo_dragon",
+    "n01697457" :	"African_crocodile",
+    "n01698640" :	"American_alligator",
+    "n01704323" :	"triceratops",
+    "n01728572" :	"thunder_snake",
+    "n01728920" :	"ringneck_snake",
+    "n01729322" :	"hognose_snake",
+    "n01729977" :	"green_snake",
+    "n01734418" :	"king_snake",
+    "n01735189" :	"garter_snake",
+    "n01737021" :	"water_snake",
+    "n01739381" :	"vine_snake",
+    "n01740131" :	"night_snake",
+    "n01742172" :	"boa_constrictor",
+    "n01744401" :	"rock_python",
+    "n01748264" :	"Indian_cobra",
+    "n01749939" :	"green_mamba",
+    "n01751748" :	"sea_snake",
+    "n01753488" :	"horned_viper",
+    "n01755581" :	"diamondback",
+    "n01756291" :	"sidewinder",
+    "n01768244" :	"trilobite",
+    "n01770081" :	"harvestman",
+    "n01770393" :	"scorpion",
+    "n01773157" :	"black_and_gold_garden_spider",
+    "n01773549" :	"barn_spider",
+    "n01773797" :	"garden_spider",
+    "n01774384" :	"black_widow",
+    "n01774750" :	"tarantula",
+    "n01775062" :	"wolf_spider",
+    "n01776313" :	"tick",
+    "n01784675" :	"centipede",
+    "n01795545" :	"black_grouse",
+    "n01796340" :	"ptarmigan",
+    "n01797886" :	"ruffed_grouse",
+    "n01798484" :	"prairie_chicken",
+    "n01806143" :	"peacock",
+    "n01806567" :	"quail",
+    "n01807496" :	"partridge",
+    "n01817953" :	"African_grey",
+    "n01818515" :	"macaw",
+    "n01819313" :	"sulphur-crested_cockatoo",
+    "n01820546" :	"lorikeet",
+    "n01824575" :	"coucal",
+    "n01828970" :	"bee_eater",
+    "n01829413" :	"hornbill",
+    "n01833805" :	"hummingbird",
+    "n01843065" :	"jacamar",
+    "n01843383" :	"toucan",
+    "n01847000" :	"drake",
+    "n01855032" :	"red-breasted_merganser",
+    "n01855672" :	"goose",
+    "n01860187" :	"black_swan",
+    "n01871265" :	"tusker",
+    "n01872401" :	"echidna",
+    "n01873310" :	"platypus",
+    "n01877812" :	"wallaby",
+    "n01882714" :	"koala",
+    "n01883070" :	"wombat",
+    "n01910747" :	"jellyfish",
+    "n01914609" :	"sea_anemone",
+    "n01917289" :	"brain_coral",
+    "n01924916" :	"flatworm",
+    "n01930112" :	"nematode",
+    "n01943899" :	"conch",
+    "n01944390" :	"snail",
+    "n01945685" :	"slug",
+    "n01950731" :	"sea_slug",
+    "n01955084" :	"chiton",
+    "n01968897" :	"chambered_nautilus",
+    "n01978287" :	"Dungeness_crab",
+    "n01978455" :	"rock_crab",
+    "n01980166" :	"fiddler_crab",
+    "n01981276" :	"king_crab",
+    "n01983481" :	"American_lobster",
+    "n01984695" :	"spiny_lobster",
+    "n01985128" :	"crayfish",
+    "n01986214" :	"hermit_crab",
+    "n01990800" :	"isopod",
+    "n02002556" :	"white_stork",
+    "n02002724" :	"black_stork",
+    "n02006656" :	"spoonbill",
+    "n02007558" :	"flamingo",
+    "n02009229" :	"little_blue_heron",
+    "n02009912" :	"American_egret",
+    "n02011460" :	"bittern",
+    "n02012849" :	"crane",
+    "n02013706" :	"limpkin",
+    "n02017213" :	"European_gallinule",
+    "n02018207" :	"American_coot",
+    "n02018795" :	"bustard",
+    "n02025239" :	"ruddy_turnstone",
+    "n02027492" :	"red-backed_sandpiper",
+    "n02028035" :	"redshank",
+    "n02033041" :	"dowitcher",
+    "n02037110" :	"oystercatcher",
+    "n02051845" :	"pelican",
+    "n02056570" :	"king_penguin",
+    "n02058221" :	"albatross",
+    "n02066245" :	"grey_whale",
+    "n02071294" :	"killer_whale",
+    "n02074367" :	"dugong",
+    "n02077923" :	"sea_lion",
+    "n02085620" :	"Chihuahua",
+    "n02085782" :	"Japanese_spaniel",
+    "n02085936" :	"Maltese_dog",
+    "n02086079" :	"Pekinese",
+    "n02086240" :	"Shih-Tzu",
+    "n02086646" :	"Blenheim_spaniel",
+    "n02086910" :	"papillon",
+    "n02087046" :	"toy_terrier",
+    "n02087394" :	"Rhodesian_ridgeback",
+    "n02088094" :	"Afghan_hound",
+    "n02088238" :	"basset",
+    "n02088364" :	"beagle",
+    "n02088466" :	"bloodhound",
+    "n02088632" :	"bluetick",
+    "n02089078" :	"black-and-tan_coonhound",
+    "n02089867" :	"Walker_hound",
+    "n02089973" :	"English_foxhound",
+    "n02090379" :	"redbone",
+    "n02090622" :	"borzoi",
+    "n02090721" :	"Irish_wolfhound",
+    "n02091032" :	"Italian_greyhound",
+    "n02091134" :	"whippet",
+    "n02091244" :	"Ibizan_hound",
+    "n02091467" :	"Norwegian_elkhound",
+    "n02091635" :	"otterhound",
+    "n02091831" :	"Saluki",
+    "n02092002" :	"Scottish_deerhound",
+    "n02092339" :	"Weimaraner",
+    "n02093256" :	"Staffordshire_bullterrier",
+    "n02093428" :	"American_Staffordshire_terrier",
+    "n02093647" :	"Bedlington_terrier",
+    "n02093754" :	"Border_terrier",
+    "n02093859" :	"Kerry_blue_terrier",
+    "n02093991" :	"Irish_terrier",
+    "n02094114" :	"Norfolk_terrier",
+    "n02094258" :	"Norwich_terrier",
+    "n02094433" :	"Yorkshire_terrier",
+    "n02095314" :	"wire-haired_fox_terrier",
+    "n02095570" :	"Lakeland_terrier",
+    "n02095889" :	"Sealyham_terrier",
+    "n02096051" :	"Airedale",
+    "n02096177" :	"cairn",
+    "n02096294" :	"Australian_terrier",
+    "n02096437" :	"Dandie_Dinmont",
+    "n02096585" :	"Boston_bull",
+    "n02097047" :	"miniature_schnauzer",
+    "n02097130" :	"giant_schnauzer",
+    "n02097209" :	"standard_schnauzer",
+    "n02097298" :	"Scotch_terrier",
+    "n02097474" :	"Tibetan_terrier",
+    "n02097658" :	"silky_terrier",
+    "n02098105" :	"soft-coated_wheaten_terrier",
+    "n02098286" :	"West_Highland_white_terrier",
+    "n02098413" :	"Lhasa",
+    "n02099267" :	"flat-coated_retriever",
+    "n02099429" :	"curly-coated_retriever",
+    "n02099601" :	"golden_retriever",
+    "n02099712" :	"Labrador_retriever",
+    "n02099849" :	"Chesapeake_Bay_retriever",
+    "n02100236" :	"German_short-haired_pointer",
+    "n02100583" :	"vizsla",
+    "n02100735" :	"English_setter",
+    "n02100877" :	"Irish_setter",
+    "n02101006" :	"Gordon_setter",
+    "n02101388" :	"Brittany_spaniel",
+    "n02101556" :	"clumber",
+    "n02102040" :	"English_springer",
+    "n02102177" :	"Welsh_springer_spaniel",
+    "n02102318" :	"cocker_spaniel",
+    "n02102480" :	"Sussex_spaniel",
+    "n02102973" :	"Irish_water_spaniel",
+    "n02104029" :	"kuvasz",
+    "n02104365" :	"schipperke",
+    "n02105056" :	"groenendael",
+    "n02105162" :	"malinois",
+    "n02105251" :	"briard",
+    "n02105412" :	"kelpie",
+    "n02105505" :	"komondor",
+    "n02105641" :	"Old_English_sheepdog",
+    "n02105855" :	"Shetland_sheepdog",
+    "n02106030" :	"collie",
+    "n02106166" :	"Border_collie",
+    "n02106382" :	"Bouvier_des_Flandres",
+    "n02106550" :	"Rottweiler",
+    "n02106662" :	"German_shepherd",
+    "n02107142" :	"Doberman",
+    "n02107312" :	"miniature_pinscher",
+    "n02107574" :	"Greater_Swiss_Mountain_dog",
+    "n02107683" :	"Bernese_mountain_dog",
+    "n02107908" :	"Appenzeller",
+    "n02108000" :	"EntleBucher",
+    "n02108089" :	"boxer",
+    "n02108422" :	"bull_mastiff",
+    "n02108551" :	"Tibetan_mastiff",
+    "n02108915" :	"French_bulldog",
+    "n02109047" :	"Great_Dane",
+    "n02109525" :	"Saint_Bernard",
+    "n02109961" :	"Eskimo_dog",
+    "n02110063" :	"malamute",
+    "n02110185" :	"Siberian_husky",
+    "n02110341" :	"dalmatian",
+    "n02110627" :	"affenpinscher",
+    "n02110806" :	"basenji",
+    "n02110958" :	"pug",
+    "n02111129" :	"Leonberg",
+    "n02111277" :	"Newfoundland",
+    "n02111500" :	"Great_Pyrenees",
+    "n02111889" :	"Samoyed",
+    "n02112018" :	"Pomeranian",
+    "n02112137" :	"chow",
+    "n02112350" :	"keeshond",
+    "n02112706" :	"Brabancon_griffon",
+    "n02113023" :	"Pembroke",
+    "n02113186" :	"Cardigan",
+    "n02113624" :	"toy_poodle",
+    "n02113712" :	"miniature_poodle",
+    "n02113799" :	"standard_poodle",
+    "n02113978" :	"Mexican_hairless",
+    "n02114367" :	"timber_wolf",
+    "n02114548" :	"white_wolf",
+    "n02114712" :	"red_wolf",
+    "n02114855" :	"coyote",
+    "n02115641" :	"dingo",
+    "n02115913" :	"dhole",
+    "n02116738" :	"African_hunting_dog",
+    "n02117135" :	"hyena",
+    "n02119022" :	"red_fox",
+    "n02119789" :	"kit_fox",
+    "n02120079" :	"Arctic_fox",
+    "n02120505" :	"grey_fox",
+    "n02123045" :	"tabby",
+    "n02123159" :	"tiger_cat",
+    "n02123394" :	"Persian_cat",
+    "n02123597" :	"Siamese_cat",
+    "n02124075" :	"Egyptian_cat",
+    "n02125311" :	"cougar",
+    "n02127052" :	"lynx",
+    "n02128385" :	"leopard",
+    "n02128757" :	"snow_leopard",
+    "n02128925" :	"jaguar",
+    "n02129165" :	"lion",
+    "n02129604" :	"tiger",
+    "n02130308" :	"cheetah",
+    "n02132136" :	"brown_bear",
+    "n02133161" :	"American_black_bear",
+    "n02134084" :	"ice_bear",
+    "n02134418" :	"sloth_bear",
+    "n02137549" :	"mongoose",
+    "n02138441" :	"meerkat",
+    "n02165105" :	"tiger_beetle",
+    "n02165456" :	"ladybug",
+    "n02167151" :	"ground_beetle",
+    "n02168699" :	"long-horned_beetle",
+    "n02169497" :	"leaf_beetle",
+    "n02172182" :	"dung_beetle",
+    "n02174001" :	"rhinoceros_beetle",
+    "n02177972" :	"weevil",
+    "n02190166" :	"fly",
+    "n02206856" :	"bee",
+    "n02219486" :	"ant",
+    "n02226429" :	"grasshopper",
+    "n02229544" :	"cricket",
+    "n02231487" :	"walking_stick",
+    "n02233338" :	"cockroach",
+    "n02236044" :	"mantis",
+    "n02256656" :	"cicada",
+    "n02259212" :	"leafhopper",
+    "n02264363" :	"lacewing",
+    "n02268443" :	"dragonfly",
+    "n02268853" :	"damselfly",
+    "n02276258" :	"admiral",
+    "n02277742" :	"ringlet",
+    "n02279972" :	"monarch",
+    "n02280649" :	"cabbage_butterfly",
+    "n02281406" :	"sulphur_butterfly",
+    "n02281787" :	"lycaenid",
+    "n02317335" :	"starfish",
+    "n02319095" :	"sea_urchin",
+    "n02321529" :	"sea_cucumber",
+    "n02325366" :	"wood_rabbit",
+    "n02326432" :	"hare",
+    "n02328150" :	"Angora",
+    "n02342885" :	"hamster",
+    "n02346627" :	"porcupine",
+    "n02356798" :	"fox_squirrel",
+    "n02361337" :	"marmot",
+    "n02363005" :	"beaver",
+    "n02364673" :	"guinea_pig",
+    "n02389026" :	"sorrel",
+    "n02391049" :	"zebra",
+    "n02395406" :	"hog",
+    "n02396427" :	"wild_boar",
+    "n02397096" :	"warthog",
+    "n02398521" :	"hippopotamus",
+    "n02403003" :	"ox",
+    "n02408429" :	"water_buffalo",
+    "n02410509" :	"bison",
+    "n02412080" :	"ram",
+    "n02415577" :	"bighorn",
+    "n02417914" :	"ibex",
+    "n02422106" :	"hartebeest",
+    "n02422699" :	"impala",
+    "n02423022" :	"gazelle",
+    "n02437312" :	"Arabian_camel",
+    "n02437616" :	"llama",
+    "n02441942" :	"weasel",
+    "n02442845" :	"mink",
+    "n02443114" :	"polecat",
+    "n02443484" :	"black-footed_ferret",
+    "n02444819" :	"otter",
+    "n02445715" :	"skunk",
+    "n02447366" :	"badger",
+    "n02454379" :	"armadillo",
+    "n02457408" :	"three-toed_sloth",
+    "n02480495" :	"orangutan",
+    "n02480855" :	"gorilla",
+    "n02481823" :	"chimpanzee",
+    "n02483362" :	"gibbon",
+    "n02483708" :	"siamang",
+    "n02484975" :	"guenon",
+    "n02486261" :	"patas",
+    "n02486410" :	"baboon",
+    "n02487347" :	"macaque",
+    "n02488291" :	"langur",
+    "n02488702" :	"colobus",
+    "n02489166" :	"proboscis_monkey",
+    "n02490219" :	"marmoset",
+    "n02492035" :	"capuchin",
+    "n02492660" :	"howler_monkey",
+    "n02493509" :	"titi",
+    "n02493793" :	"spider_monkey",
+    "n02494079" :	"squirrel_monkey",
+    "n02497673" :	"Madagascar_cat",
+    "n02500267" :	"indri",
+    "n02504013" :	"Indian_elephant",
+    "n02504458" :	"African_elephant",
+    "n02509815" :	"lesser_panda",
+    "n02510455" :	"giant_panda",
+    "n02514041" :	"barracouta",
+    "n02526121" :	"eel",
+    "n02536864" :	"coho",
+    "n02606052" :	"rock_beauty",
+    "n02607072" :	"anemone_fish",
+    "n02640242" :	"sturgeon",
+    "n02641379" :	"gar",
+    "n02643566" :	"lionfish",
+    "n02655020" :	"puffer",
+    "n02666196" :	"abacus",
+    "n02667093" :	"abaya",
+    "n02669723" :	"academic_gown",
+    "n02672831" :	"accordion",
+    "n02676566" :	"acoustic_guitar",
+    "n02687172" :	"aircraft_carrier",
+    "n02690373" :	"airliner",
+    "n02692877" :	"airship",
+    "n02699494" :	"altar",
+    "n02701002" :	"ambulance",
+    "n02704792" :	"amphibian",
+    "n02708093" :	"analog_clock",
+    "n02727426" :	"apiary",
+    "n02730930" :	"apron",
+    "n02747177" :	"ashcan",
+    "n02749479" :	"assault_rifle",
+    "n02769748" :	"backpack",
+    "n02776631" :	"bakery",
+    "n02777292" :	"balance_beam",
+    "n02782093" :	"balloon",
+    "n02783161" :	"ballpoint",
+    "n02786058" :	"Band_Aid",
+    "n02787622" :	"banjo",
+    "n02788148" :	"bannister",
+    "n02790996" :	"barbell",
+    "n02791124" :	"barber_chair",
+    "n02791270" :	"barbershop",
+    "n02793495" :	"barn",
+    "n02794156" :	"barometer",
+    "n02795169" :	"barrel",
+    "n02797295" :	"barrow",
+    "n02799071" :	"baseball",
+    "n02802426" :	"basketball",
+    "n02804414" :	"bassinet",
+    "n02804610" :	"bassoon",
+    "n02807133" :	"bathing_cap",
+    "n02808304" :	"bath_towel",
+    "n02808440" :	"bathtub",
+    "n02814533" :	"beach_wagon",
+    "n02814860" :	"beacon",
+    "n02815834" :	"beaker",
+    "n02817516" :	"bearskin",
+    "n02823428" :	"beer_bottle",
+    "n02823750" :	"beer_glass",
+    "n02825657" :	"bell_cote",
+    "n02834397" :	"bib",
+    "n02835271" :	"bicycle-built-for-two",
+    "n02837789" :	"bikini",
+    "n02840245" :	"binder",
+    "n02841315" :	"binoculars",
+    "n02843684" :	"birdhouse",
+    "n02859443" :	"boathouse",
+    "n02860847" :	"bobsled",
+    "n02865351" :	"bolo_tie",
+    "n02869837" :	"bonnet",
+    "n02870880" :	"bookcase",
+    "n02871525" :	"bookshop",
+    "n02877765" :	"bottlecap",
+    "n02879718" :	"bow",
+    "n02883205" :	"bow_tie",
+    "n02892201" :	"brass",
+    "n02892767" :	"brassiere",
+    "n02894605" :	"breakwater",
+    "n02895154" :	"breastplate",
+    "n02906734" :	"broom",
+    "n02909870" :	"bucket",
+    "n02910353" :	"buckle",
+    "n02916936" :	"bulletproof_vest",
+    "n02917067" :	"bullet_train",
+    "n02927161" :	"butcher_shop",
+    "n02930766" :	"cab",
+    "n02939185" :	"caldron",
+    "n02948072" :	"candle",
+    "n02950826" :	"cannon",
+    "n02951358" :	"canoe",
+    "n02951585" :	"can_opener",
+    "n02963159" :	"cardigan",
+    "n02965783" :	"car_mirror",
+    "n02966193" :	"carousel",
+    "n02966687" :	"carpenter's_kit",
+    "n02971356" :	"carton",
+    "n02974003" :	"car_wheel",
+    "n02977058" :	"cash_machine",
+    "n02978881" :	"cassette",
+    "n02979186" :	"cassette_player",
+    "n02980441" :	"castle",
+    "n02981792" :	"catamaran",
+    "n02988304" :	"CD_player",
+    "n02992211" :	"cello",
+    "n02992529" :	"cellular_telephone",
+    "n02999410" :	"chain",
+    "n03000134" :	"chainlink_fence",
+    "n03000247" :	"chain_mail",
+    "n03000684" :	"chain_saw",
+    "n03014705" :	"chest",
+    "n03016953" :	"chiffonier",
+    "n03017168" :	"chime",
+    "n03018349" :	"china_cabinet",
+    "n03026506" :	"Christmas_stocking",
+    "n03028079" :	"church",
+    "n03032252" :	"cinema",
+    "n03041632" :	"cleaver",
+    "n03042490" :	"cliff_dwelling",
+    "n03045698" :	"cloak",
+    "n03047690" :	"clog",
+    "n03062245" :	"cocktail_shaker",
+    "n03063599" :	"coffee_mug",
+    "n03063689" :	"coffeepot",
+    "n03065424" :	"coil",
+    "n03075370" :	"combination_lock",
+    "n03085013" :	"computer_keyboard",
+    "n03089624" :	"confectionery",
+    "n03095699" :	"container_ship",
+    "n03100240" :	"convertible",
+    "n03109150" :	"corkscrew",
+    "n03110669" :	"cornet",
+    "n03124043" :	"cowboy_boot",
+    "n03124170" :	"cowboy_hat",
+    "n03125729" :	"cradle",
+    "n03126707" :	"crane",
+    "n03127747" :	"crash_helmet",
+    "n03127925" :	"crate",
+    "n03131574" :	"crib",
+    "n03133878" :	"Crock_Pot",
+    "n03134739" :	"croquet_ball",
+    "n03141823" :	"crutch",
+    "n03146219" :	"cuirass",
+    "n03160309" :	"dam",
+    "n03179701" :	"desk",
+    "n03180011" :	"desktop_computer",
+    "n03187595" :	"dial_telephone",
+    "n03188531" :	"diaper",
+    "n03196217" :	"digital_clock",
+    "n03197337" :	"digital_watch",
+    "n03201208" :	"dining_table",
+    "n03207743" :	"dishrag",
+    "n03207941" :	"dishwasher",
+    "n03208938" :	"disk_brake",
+    "n03216828" :	"dock",
+    "n03218198" :	"dogsled",
+    "n03220513" :	"dome",
+    "n03223299" :	"doormat",
+    "n03240683" :	"drilling_platform",
+    "n03249569" :	"drum",
+    "n03250847" :	"drumstick",
+    "n03255030" :	"dumbbell",
+    "n03259280" :	"Dutch_oven",
+    "n03271574" :	"electric_fan",
+    "n03272010" :	"electric_guitar",
+    "n03272562" :	"electric_locomotive",
+    "n03290653" :	"entertainment_center",
+    "n03291819" :	"envelope",
+    "n03297495" :	"espresso_maker",
+    "n03314780" :	"face_powder",
+    "n03325584" :	"feather_boa",
+    "n03337140" :	"file",
+    "n03344393" :	"fireboat",
+    "n03345487" :	"fire_engine",
+    "n03347037" :	"fire_screen",
+    "n03355925" :	"flagpole",
+    "n03372029" :	"flute",
+    "n03376595" :	"folding_chair",
+    "n03379051" :	"football_helmet",
+    "n03384352" :	"forklift",
+    "n03388043" :	"fountain",
+    "n03388183" :	"fountain_pen",
+    "n03388549" :	"four-poster",
+    "n03393912" :	"freight_car",
+    "n03394916" :	"French_horn",
+    "n03400231" :	"frying_pan",
+    "n03404251" :	"fur_coat",
+    "n03417042" :	"garbage_truck",
+    "n03424325" :	"gasmask",
+    "n03425413" :	"gas_pump",
+    "n03443371" :	"goblet",
+    "n03444034" :	"go-kart",
+    "n03445777" :	"golf_ball",
+    "n03445924" :	"golfcart",
+    "n03447447" :	"gondola",
+    "n03447721" :	"gong",
+    "n03450230" :	"gown",
+    "n03452741" :	"grand_piano",
+    "n03457902" :	"greenhouse",
+    "n03459775" :	"grille",
+    "n03461385" :	"grocery_store",
+    "n03467068" :	"guillotine",
+    "n03476684" :	"hair_slide",
+    "n03476991" :	"hair_spray",
+    "n03478589" :	"half_track",
+    "n03481172" :	"hammer",
+    "n03482405" :	"hamper",
+    "n03483316" :	"hand_blower",
+    "n03485407" :	"hand-held_computer",
+    "n03485794" :	"handkerchief",
+    "n03492542" :	"hard_disc",
+    "n03494278" :	"harmonica",
+    "n03495258" :	"harp",
+    "n03496892" :	"harvester",
+    "n03498962" :	"hatchet",
+    "n03527444" :	"holster",
+    "n03529860" :	"home_theater",
+    "n03530642" :	"honeycomb",
+    "n03532672" :	"hook",
+    "n03534580" :	"hoopskirt",
+    "n03535780" :	"horizontal_bar",
+    "n03538406" :	"horse_cart",
+    "n03544143" :	"hourglass",
+    "n03584254" :	"iPod",
+    "n03584829" :	"iron",
+    "n03590841" :	"jack-o'-lantern",
+    "n03594734" :	"jean",
+    "n03594945" :	"jeep",
+    "n03595614" :	"jersey",
+    "n03598930" :	"jigsaw_puzzle",
+    "n03599486" :	"jinrikisha",
+    "n03602883" :	"joystick",
+    "n03617480" :	"kimono",
+    "n03623198" :	"knee_pad",
+    "n03627232" :	"knot",
+    "n03630383" :	"lab_coat",
+    "n03633091" :	"ladle",
+    "n03637318" :	"lampshade",
+    "n03642806" :	"laptop",
+    "n03649909" :	"lawn_mower",
+    "n03657121" :	"lens_cap",
+    "n03658185" :	"letter_opener",
+    "n03661043" :	"library",
+    "n03662601" :	"lifeboat",
+    "n03666591" :	"lighter",
+    "n03670208" :	"limousine",
+    "n03673027" :	"liner",
+    "n03676483" :	"lipstick",
+    "n03680355" :	"Loafer",
+    "n03690938" :	"lotion",
+    "n03691459" :	"loudspeaker",
+    "n03692522" :	"loupe",
+    "n03697007" :	"lumbermill",
+    "n03706229" :	"magnetic_compass",
+    "n03709823" :	"mailbag",
+    "n03710193" :	"mailbox",
+    "n03710637" :	"maillot",
+    "n03710721" :	"maillot",
+    "n03717622" :	"manhole_cover",
+    "n03720891" :	"maraca",
+    "n03721384" :	"marimba",
+    "n03724870" :	"mask",
+    "n03729826" :	"matchstick",
+    "n03733131" :	"maypole",
+    "n03733281" :	"maze",
+    "n03733805" :	"measuring_cup",
+    "n03742115" :	"medicine_chest",
+    "n03743016" :	"megalith",
+    "n03759954" :	"microphone",
+    "n03761084" :	"microwave",
+    "n03763968" :	"military_uniform",
+    "n03764736" :	"milk_can",
+    "n03769881" :	"minibus",
+    "n03770439" :	"miniskirt",
+    "n03770679" :	"minivan",
+    "n03773504" :	"missile",
+    "n03775071" :	"mitten",
+    "n03775546" :	"mixing_bowl",
+    "n03776460" :	"mobile_home",
+    "n03777568" :	"Model_T",
+    "n03777754" :	"modem",
+    "n03781244" :	"monastery",
+    "n03782006" :	"monitor",
+    "n03785016" :	"moped",
+    "n03786901" :	"mortar",
+    "n03787032" :	"mortarboard",
+    "n03788195" :	"mosque",
+    "n03788365" :	"mosquito_net",
+    "n03791053" :	"motor_scooter",
+    "n03792782" :	"mountain_bike",
+    "n03792972" :	"mountain_tent",
+    "n03793489" :	"mouse",
+    "n03794056" :	"mousetrap",
+    "n03796401" :	"moving_van",
+    "n03803284" :	"muzzle",
+    "n03804744" :	"nail",
+    "n03814639" :	"neck_brace",
+    "n03814906" :	"necklace",
+    "n03825788" :	"nipple",
+    "n03832673" :	"notebook",
+    "n03837869" :	"obelisk",
+    "n03838899" :	"oboe",
+    "n03840681" :	"ocarina",
+    "n03841143" :	"odometer",
+    "n03843555" :	"oil_filter",
+    "n03854065" :	"organ",
+    "n03857828" :	"oscilloscope",
+    "n03866082" :	"overskirt",
+    "n03868242" :	"oxcart",
+    "n03868863" :	"oxygen_mask",
+    "n03871628" :	"packet",
+    "n03873416" :	"paddle",
+    "n03874293" :	"paddlewheel",
+    "n03874599" :	"padlock",
+    "n03876231" :	"paintbrush",
+    "n03877472" :	"pajama",
+    "n03877845" :	"palace",
+    "n03884397" :	"panpipe",
+    "n03887697" :	"paper_towel",
+    "n03888257" :	"parachute",
+    "n03888605" :	"parallel_bars",
+    "n03891251" :	"park_bench",
+    "n03891332" :	"parking_meter",
+    "n03895866" :	"passenger_car",
+    "n03899768" :	"patio",
+    "n03902125" :	"pay-phone",
+    "n03903868" :	"pedestal",
+    "n03908618" :	"pencil_box",
+    "n03908714" :	"pencil_sharpener",
+    "n03916031" :	"perfume",
+    "n03920288" :	"Petri_dish",
+    "n03924679" :	"photocopier",
+    "n03929660" :	"pick",
+    "n03929855" :	"pickelhaube",
+    "n03930313" :	"picket_fence",
+    "n03930630" :	"pickup",
+    "n03933933" :	"pier",
+    "n03935335" :	"piggy_bank",
+    "n03937543" :	"pill_bottle",
+    "n03938244" :	"pillow",
+    "n03942813" :	"ping-pong_ball",
+    "n03944341" :	"pinwheel",
+    "n03947888" :	"pirate",
+    "n03950228" :	"pitcher",
+    "n03954731" :	"plane",
+    "n03956157" :	"planetarium",
+    "n03958227" :	"plastic_bag",
+    "n03961711" :	"plate_rack",
+    "n03967562" :	"plow",
+    "n03970156" :	"plunger",
+    "n03976467" :	"Polaroid_camera",
+    "n03976657" :	"pole",
+    "n03977966" :	"police_van",
+    "n03980874" :	"poncho",
+    "n03982430" :	"pool_table",
+    "n03983396" :	"pop_bottle",
+    "n03991062" :	"pot",
+    "n03992509" :	"potter's_wheel",
+    "n03995372" :	"power_drill",
+    "n03998194" :	"prayer_rug",
+    "n04004767" :	"printer",
+    "n04005630" :	"prison",
+    "n04008634" :	"projectile",
+    "n04009552" :	"projector",
+    "n04019541" :	"puck",
+    "n04023962" :	"punching_bag",
+    "n04026417" :	"purse",
+    "n04033901" :	"quill",
+    "n04033995" :	"quilt",
+    "n04037443" :	"racer",
+    "n04039381" :	"racket",
+    "n04040759" :	"radiator",
+    "n04041544" :	"radio",
+    "n04044716" :	"radio_telescope",
+    "n04049303" :	"rain_barrel",
+    "n04065272" :	"recreational_vehicle",
+    "n04067472" :	"reel",
+    "n04069434" :	"reflex_camera",
+    "n04070727" :	"refrigerator",
+    "n04074963" :	"remote_control",
+    "n04081281" :	"restaurant",
+    "n04086273" :	"revolver",
+    "n04090263" :	"rifle",
+    "n04099969" :	"rocking_chair",
+    "n04111531" :	"rotisserie",
+    "n04116512" :	"rubber_eraser",
+    "n04118538" :	"rugby_ball",
+    "n04118776" :	"rule",
+    "n04120489" :	"running_shoe",
+    "n04125021" :	"safe",
+    "n04127249" :	"safety_pin",
+    "n04131690" :	"saltshaker",
+    "n04133789" :	"sandal",
+    "n04136333" :	"sarong",
+    "n04141076" :	"sax",
+    "n04141327" :	"scabbard",
+    "n04141975" :	"scale",
+    "n04146614" :	"school_bus",
+    "n04147183" :	"schooner",
+    "n04149813" :	"scoreboard",
+    "n04152593" :	"screen",
+    "n04153751" :	"screw",
+    "n04154565" :	"screwdriver",
+    "n04162706" :	"seat_belt",
+    "n04179913" :	"sewing_machine",
+    "n04192698" :	"shield",
+    "n04200800" :	"shoe_shop",
+    "n04201297" :	"shoji",
+    "n04204238" :	"shopping_basket",
+    "n04204347" :	"shopping_cart",
+    "n04208210" :	"shovel",
+    "n04209133" :	"shower_cap",
+    "n04209239" :	"shower_curtain",
+    "n04228054" :	"ski",
+    "n04229816" :	"ski_mask",
+    "n04235860" :	"sleeping_bag",
+    "n04238763" :	"slide_rule",
+    "n04239074" :	"sliding_door",
+    "n04243546" :	"slot",
+    "n04251144" :	"snorkel",
+    "n04252077" :	"snowmobile",
+    "n04252225" :	"snowplow",
+    "n04254120" :	"soap_dispenser",
+    "n04254680" :	"soccer_ball",
+    "n04254777" :	"sock",
+    "n04258138" :	"solar_dish",
+    "n04259630" :	"sombrero",
+    "n04263257" :	"soup_bowl",
+    "n04264628" :	"space_bar",
+    "n04265275" :	"space_heater",
+    "n04266014" :	"space_shuttle",
+    "n04270147" :	"spatula",
+    "n04273569" :	"speedboat",
+    "n04275548" :	"spider_web",
+    "n04277352" :	"spindle",
+    "n04285008" :	"sports_car",
+    "n04286575" :	"spotlight",
+    "n04296562" :	"stage",
+    "n04310018" :	"steam_locomotive",
+    "n04311004" :	"steel_arch_bridge",
+    "n04311174" :	"steel_drum",
+    "n04317175" :	"stethoscope",
+    "n04325704" :	"stole",
+    "n04326547" :	"stone_wall",
+    "n04328186" :	"stopwatch",
+    "n04330267" :	"stove",
+    "n04332243" :	"strainer",
+    "n04335435" :	"streetcar",
+    "n04336792" :	"stretcher",
+    "n04344873" :	"studio_couch",
+    "n04346328" :	"stupa",
+    "n04347754" :	"submarine",
+    "n04350905" :	"suit",
+    "n04355338" :	"sundial",
+    "n04355933" :	"sunglass",
+    "n04356056" :	"sunglasses",
+    "n04357314" :	"sunscreen",
+    "n04366367" :	"suspension_bridge",
+    "n04367480" :	"swab",
+    "n04370456" :	"sweatshirt",
+    "n04371430" :	"swimming_trunks",
+    "n04371774" :	"swing",
+    "n04372370" :	"switch",
+    "n04376876" :	"syringe",
+    "n04380533" :	"table_lamp",
+    "n04389033" :	"tank",
+    "n04392985" :	"tape_player",
+    "n04398044" :	"teapot",
+    "n04399382" :	"teddy",
+    "n04404412" :	"television",
+    "n04409515" :	"tennis_ball",
+    "n04417672" :	"thatch",
+    "n04418357" :	"theater_curtain",
+    "n04423845" :	"thimble",
+    "n04428191" :	"thresher",
+    "n04429376" :	"throne",
+    "n04435653" :	"tile_roof",
+    "n04442312" :	"toaster",
+    "n04443257" :	"tobacco_shop",
+    "n04447861" :	"toilet_seat",
+    "n04456115" :	"torch",
+    "n04458633" :	"totem_pole",
+    "n04461696" :	"tow_truck",
+    "n04462240" :	"toyshop",
+    "n04465501" :	"tractor",
+    "n04467665" :	"trailer_truck",
+    "n04476259" :	"tray",
+    "n04479046" :	"trench_coat",
+    "n04482393" :	"tricycle",
+    "n04483307" :	"trimaran",
+    "n04485082" :	"tripod",
+    "n04486054" :	"triumphal_arch",
+    "n04487081" :	"trolleybus",
+    "n04487394" :	"trombone",
+    "n04493381" :	"tub",
+    "n04501370" :	"turnstile",
+    "n04505470" :	"typewriter_keyboard",
+    "n04507155" :	"umbrella",
+    "n04509417" :	"unicycle",
+    "n04515003" :	"upright",
+    "n04517823" :	"vacuum",
+    "n04522168" :	"vase",
+    "n04523525" :	"vault",
+    "n04525038" :	"velvet",
+    "n04525305" :	"vending_machine",
+    "n04532106" :	"vestment",
+    "n04532670" :	"viaduct",
+    "n04536866" :	"violin",
+    "n04540053" :	"volleyball",
+    "n04542943" :	"waffle_iron",
+    "n04548280" :	"wall_clock",
+    "n04548362" :	"wallet",
+    "n04550184" :	"wardrobe",
+    "n04552348" :	"warplane",
+    "n04553703" :	"washbasin",
+    "n04554684" :	"washer",
+    "n04557648" :	"water_bottle",
+    "n04560804" :	"water_jug",
+    "n04562935" :	"water_tower",
+    "n04579145" :	"whiskey_jug",
+    "n04579432" :	"whistle",
+    "n04584207" :	"wig",
+    "n04589890" :	"window_screen",
+    "n04590129" :	"window_shade",
+    "n04591157" :	"Windsor_tie",
+    "n04591713" :	"wine_bottle",
+    "n04592741" :	"wing",
+    "n04596742" :	"wok",
+    "n04597913" :	"wooden_spoon",
+    "n04599235" :	"wool",
+    "n04604644" :	"worm_fence",
+    "n04606251" :	"wreck",
+    "n04612504" :	"yawl",
+    "n04613696" :	"yurt",
+    "n06359193" :	"web_site",
+    "n06596364" :	"comic_book",
+    "n06785654" :	"crossword_puzzle",
+    "n06794110" :	"street_sign",
+    "n06874185" :	"traffic_light",
+    "n07248320" :	"book_jacket",
+    "n07565083" :	"menu",
+    "n07579787" :	"plate",
+    "n07583066" :	"guacamole",
+    "n07584110" :	"consomme",
+    "n07590611" :	"hot_pot",
+    "n07613480" :	"trifle",
+    "n07614500" :	"ice_cream",
+    "n07615774" :	"ice_lolly",
+    "n07684084" :	"French_loaf",
+    "n07693725" :	"bagel",
+    "n07695742" :	"pretzel",
+    "n07697313" :	"cheeseburger",
+    "n07697537" :	"hotdog",
+    "n07711569" :	"mashed_potato",
+    "n07714571" :	"head_cabbage",
+    "n07714990" :	"broccoli",
+    "n07715103" :	"cauliflower",
+    "n07716358" :	"zucchini",
+    "n07716906" :	"spaghetti_squash",
+    "n07717410" :	"acorn_squash",
+    "n07717556" :	"butternut_squash",
+    "n07718472" :	"cucumber",
+    "n07718747" :	"artichoke",
+    "n07720875" :	"bell_pepper",
+    "n07730033" :	"cardoon",
+    "n07734744" :	"mushroom",
+    "n07742313" :	"Granny_Smith",
+    "n07745940" :	"strawberry",
+    "n07747607" :	"orange",
+    "n07749582" :	"lemon",
+    "n07753113" :	"fig",
+    "n07753275" :	"pineapple",
+    "n07753592" :	"banana",
+    "n07754684" :	"jackfruit",
+    "n07760859" :	"custard_apple",
+    "n07768694" :	"pomegranate",
+    "n07802026" :	"hay",
+    "n07831146" :	"carbonara",
+    "n07836838" :	"chocolate_sauce",
+    "n07860988" :	"dough",
+    "n07871810" :	"meat_loaf",
+    "n07873807" :	"pizza",
+    "n07875152" :	"potpie",
+    "n07880968" :	"burrito",
+    "n07892512" :	"red_wine",
+    "n07920052" :	"espresso",
+    "n07930864" :	"cup",
+    "n07932039" :	"eggnog",
+    "n09193705" :	"alp",
+    "n09229709" :	"bubble",
+    "n09246464" :	"cliff",
+    "n09256479" :	"coral_reef",
+    "n09288635" :	"geyser",
+    "n09332890" :	"lakeside",
+    "n09399592" :	"promontory",
+    "n09421951" :	"sandbar",
+    "n09428293" :	"seashore",
+    "n09468604" :	"valley",
+    "n09472597" :	"volcano",
+    "n09835506" :	"ballplayer",
+    "n10148035" :	"groom",
+    "n10565667" :	"scuba_diver",
+    "n11879895" :	"rapeseed",
+    "n11939491" :	"daisy",
+    "n12057211" :	"yellow_lady's_slipper",
+    "n12144580" :	"corn",
+    "n12267677" :	"acorn",
+    "n12620546" :	"hip",
+    "n12768682" :	"buckeye",
+    "n12985857" :	"coral_fungus",
+    "n12998815" :	"agaric",
+    "n13037406" :	"gyromitra",
+    "n13040303" :	"stinkhorn",
+    "n13044778" :	"earthstar",
+    "n13052670" :	"hen-of-the-woods",
+    "n13054560" :	"bolete",
+    "n13133613" :	"ear",
+    "n15075141" :	"toilet_tissu"
+}