Concept is a technique that leverages CLIP and BERTopic-based techniques to perform Concept Modeling on images.
Since topics are part of conversations and text, they do not represent the context of images well. Therefore, these clusters of images are
referred to as ‘Concepts’ instead of the traditional ‘Topics’.
Thus, Concept Modeling takes inspiration from topic modeling techniques
to cluster images, find common concepts and model them both visually
using images and textually using topic representations.
Installation, with sentence-transformers, can be done using pypi:
pip install concept
First, we need to download and extract 25.000 images from Unsplash used in the sentence-transformers
import os import zipfile from tqdm import tqdm from PIL import Image from sentence_transformers import util # 25k images from Unsplash img_folder = 'photos/' if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0: os.makedirs(img_folder, exist_ok=True) photo_filename = 'unsplash-25k-photos.zip' if not os.path.exists(photo_filename): #Download dataset if does not exist util.http_get('http://sbert.net/datasets/'+photo_filename, photo_filename) #Extract all images with zipfile.ZipFile(photo_filename, 'r') as zf: for member in tqdm(zf.infolist(), desc='Extracting'): zf.extract(member, img_folder) images = [Image.open("photos/"+filepath) for filepath in tqdm(img_names)]
Next, we only need to pass images to Concept:
from concept import ConceptModel concept_model = ConceptModel() concepts = concept_model.fit_transform(images)
The resulting concepts can be visualized through
However, to get the full experience, we need to label the concept clusters with topics. To do this,
we need to create a vocabulary:
from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data'] vectorizer = TfidfVectorizer(ngram_range=(1, 2)).fit(docs) words = vectorizer.get_feature_names() words = [words[index] for index in np.argpartition(vectorizer.idf_, -50_000)[-50_000:]]
Then, we can pass in the resulting
words to Concept:
from concept import ConceptModel concept_model = ConceptModel() concepts = concept_model.fit_transform(images, docs=words)
Again, the resulting concepts can be visualized. This time however, we can also see the generated topics
Concept(embedding_model="clip-ViT-B-32-multilingual-v1") to select a model that supports 50+ languages.