This script is meant to help you quickly build custom computer vision datasets for classification, detection or segmentation: it doesn't do the labeling for you. But it takes care of the steps beforehand:
- Define your set of classes
- Scrape the data for each class
- Rename the files
- Organize the folder structure
- Upload the data to makesense.ai for the annotation (objection detection or segmentation)
If you opt for the detection task, the script uploads the downloaded images with the corresponding labels to http://makesense.ai (or locally to http://localhost:3000) so that all you have to do in annotate yourself.
Once the annotation is done, your labels can be exported and you'll be ready to train your awesome models.
pip install google_images_download
pip install -U selenium
- ChromeDriver 77.0.3865.40
If you wish to run Make Sense locally:
# clone repository git clone https://github.com/SkalskiP/make-sense.git # navigate to main dir cd make-sense # install dependencies npm install # serve with hot reload at localhost:3000 npm start
When you run the script, you can specify the following arguments:
output_directory: the root folder when images are downloaded
limit: the maximum number of downloaded images per category
delete_history: whether you choose to erase previous downloads or not
task: classification, detection or segmentation
driver: path to chrome driver
run_local: whether or not to use makesense locally
python dataset_builder.py --limit 20 --delete_history yes
Once the script runs, you'll be asked to define your classes (or queries)
Here's what the output looks like after the download:
This only works if you choose a detection or segmentation
Make Sense is an awesome open source webapp that lets you easily label your image dataset for tasks such as localization.
In order to use this tool, I'll be running it locally and interface with it using Selenium: Once the dataset is downloaded, Selenium opens up a Chrome browser, upload the images to the app and fill in the label list: this ultimately allows you to annotate.
Please feel free to contribute ! Report any bugs in the issue section, or request any feature you'd like to see shipped:
- Accelerate the download of images via multiprocessing
- Apply a quality check on the images
- Integrate automatic tagging using pre-trained networks