Where can I view the ImageNet classes as a hierarchy on WordNet?

Question

I always find a list of classes on Github that represent the synset ID and name of each Imagenet class label. I need to view the WordNet hierarchy of ImageNet as a tree so I can prune some classes that I don't need based on conceptual relevance. This source is not good enough as it is not easy to look through.

Answer 1

See this website: https://observablehq.com/@mbostock/imagenet-hierarchy

You can also create the tree by yourself using the metadata in the ImageNet devkit.

Answer 2

You can use nltk package in Python to get the hypernyms (parents) of each class. The following code saves the 6 most super-ordinate categories for each class label.

I downloaded the LOC_synset_mapping.txt file from here.

from nltk.corpus import wordnet as wn
import pandas as pd
import numpy as np

#-----------------------------
#read synset ids for classes:
#-----------------------------

map_fname=base_dir+'/LOC_synset_mapping.txt'
synset_ids_1000=np.zeros(1000)
l=0;
with open(map_fname, 'r') as f:
    for line in f:
        synset_id_s = line.split()[0]
        synset_id=int(synset_id_s[1:])
        synset_ids_1000[l]=synset_id
        l=l+1

#-------------------------

#create the list of parents for all classes:
#-----------------------------


all_list=[["-" for j in range(7)] for i in range(1000)]
for i in range(1000):
    synset=wn.synset_from_pos_and_offset('n',int(synset_ids_1000[i]))
    hyper_list=[]
    while synset.hypernyms():
        synset = synset.hypernyms()[0]
        hyper_list.append(synset.name())
    hyper_list.insert(0,'null')
    hyper_list.insert(0,'null')
    all_list[i][:]=hyper_list[:-7:-1]

df=pd.DataFrame(all_list)

To see parents of the a specific class, e.g. 11th class:

df.iloc[10]

0             entity.n.01
1    physical_entity.n.01
2             object.n.01
3              whole.n.02
4       living_thing.n.01
5           organism.n.01
6             animal.n.01

to see number of different unique labels at a specific depth, e.g. 3:

df.iloc[:,3].value_counts()

whole.n.02                   946
substance.n.07                22
solid.n.01                    16
geological_formation.n.01      9
person.n.01                    3
sign.n.02                      1
signal.n.01                    1
shape.n.02                     1
substance.n.01                 1