Image tagger algorithm that labels an input image with a word or text that describes it the most from a predefined possible set of the text labels.
This repo presents code of deep learning based algorithm for
image tagging task. The algorithm can label an input image with
corresponding text tag with high accuracy and efficiency. You just have to
provide a set of possible text tags and the algorithm will match an input image
with a corresponding tag. The text tag can be a single word or phrase, which you
should provide in setting.yaml file under labels key.
By default, the Unknown label will be returned if model's confidence is less
than prediction-threshold in setting.yaml file. You can check other possible
settings in the setting.yaml file.
First install requirements:
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
To test the model you can either run:
python run.py --image-path ./data/0.jpg
Or you can test it through web app:
streamlit run app.py
Or you can see the example code in tutorial.ipynb jupyter notebook
Or incorporate this model in your project using this code:
import cv2
from model import Model
model = Model()
image = cv2.imread('./data/0.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
best_match_label = model.predict(image=image)['labels'][0]
print('Image label is: ', best_match_label)Below are the prediction results of the model. As you see the model accurately
labels images with tags from possible set of the text labels. You can find code
that produces that result in tutorial.ipynb jupyter notebook.
The further possible enhancements that can be done in the future are: batch processing support for speedup in case of request with multiple images, threshold fine-tuning for specific data, ect.
Soso Sukhitashvili - Linkedin