# google-images-download: a Selenium scraper for building image datasets > A Python package that drives a real Chrome through Selenium to pull hundreds of images from a Google Images search, complete with color filtering, and a licensing disclaimer that deserves more attention than the code. **hardikvasa/google-images-download** — Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code! - Repository: https://github.com/hardikvasa/google-images-download - Stars: 8,698 · Forks: 2,082 - Language: Python - License: MIT - Published: 2026-10-06 · Updated: 2026-10-06 · Language: en - Canonical page: https://hysenlabs.com/projects/hardikvasa-google-images-download ## Why this scraper needs a whole browser The defining fact about this project is in the Requirements section, and it is a constraint rather than an installation note: Chrome or Chromium is required for the tool to work, because Google Images now requires JavaScript execution, which the README says makes Selenium with Chrome mandatory for all downloads. That single sentence explains the design. The obvious approach, and the one most pre-2018 scripts used, was to fetch a search results page and parse the HTML for image URLs. Once a search page renders its results with JavaScript, an HTTP client receives a page with no images in it, and the scraper needs a browser that will run the page and produce a DOM. Selenium is the bridge, and the browser is the thing doing the actual work. The README states that the tool automatically downloads and manages chromedriver using `webdriver-manager`, which removes the traditional annoyance of matching a driver build to a Chrome version. It is still a browser under automation, running headless or not, and everything that implies about memory, startup time and fragility follows. Browser installation is delegated to the platform, with one documented command per operating system: ```bash apt-get install chromium-browser brew install --cask google-chrome ``` Those are the Linux and macOS routes named in the README, with Windows handled by downloading Chrome from Google's site instead. ## A tiny dependency list with a large transitive footprint The dependency surface as declared is two packages, and the file that states it is short enough to show in full: ```bash selenium webdriver-manager ``` Those two lines are the entire contents of `requirements.txt`. The pinned versions live one level up, in `pyproject.toml`, under the project dependencies: ```toml dependencies = [ "selenium>=4.0.0", "webdriver-manager>=3.8.0", ] ``` Two declared packages, but the real footprint is much larger, because Selenium is a client library and the browser plus chromedriver sit behind it. That is the shape of the trade: the dependency list looks trivially small while the thing that actually does the work is a Chrome installation that has to be present and roughly current. The build itself is unremarkable, which is a good sign for installability: ```toml requires = ["setuptools>=45", "wheel", "setuptools_scm[toml]>=6.2"] build-backend = "setuptools.build_meta" ``` Note the `setuptools_scm` requirement. That build plugin derives the version from the repository tags rather than from a hardcoded number, which is why there is no release list to consult and why the package version only means something relative to a checkout. ## Packaging details that disagree with each other The repository carries two build configurations and they do not agree, which is the kind of thing worth knowing before you file a bug. `pyproject.toml` states the project at version 3.0.1, with `requires-python` set to 3.7 or later, and classifiers listing Python 3.7 through 3.12. `setup.py` also declares version 3.0.1, so the number is consistent. But the legacy classifiers in `setup.py` list Python 2.7 and 3.3 through 3.6, which contradicts the modern list entirely. There is a second mismatch in the documentation URLs. The README links its documentation to `hardikvasa.github.io/google-images-download`, with separate pages for installation, input arguments and examples, while the `Documentation` entry in `pyproject.toml` points at a readthedocs.io address. One of the two is the live one. The console entry point is the part that matters for use, and it is declared in `pyproject.toml`: ```toml googleimagesdownload = "google_images_download.google_images_download:main" ``` So the tool installs a command named `googleimagesdownload` that resolves to a `main` function inside the package. What that function accepts is documented on the arguments page rather than in the README, which points at a homepage, an installation page, an input arguments page and an examples page rather than inlining any of them. If you are evaluating this project, the arguments page is the document that matters, because the colour filtering and search term handling all live there. The repository structure is otherwise conventional: `google_images_download/` for the package, `tests/` for the test suite, `docs/` for the documentation source, `mkdocs.yml` for building it, `images/` for screenshots, and `PUBLISHING.md` for release process. ## What the README says about copyright, in detail The Disclaimer section is longer than most of this project's install instructions, and it is the part any serious user should read twice. The README says the program lets you download tons of images from Google, and asks that you not download or use any image that violates its copyright terms. It then makes the structural point clearly: Google Images is a search engine that indexes images, it does not produce its own images, and it therefore does not own copyright in any of them. The copyright belongs to the original creators. It also states that images published in the United States are automatically copyrighted by their owners even without an explicit copyright notice, and that reproducing copyrighted images without permission, outside of fair use cases, risks lawyer's warnings, cease-and-desist letters and lawsuits. The stated permitted use is educational. That is an unusually direct warning for a repository with this many users, and it is not a formality. A scraper that works is a scraper that will work at scale, and the person who decided to run it at scale may not be the person who read the disclaimer. The tooling question and the legal question are separate, and this project answers the first thoroughly while telling you plainly that the second is yours. ## Where a dedicated dataset beats a scraper The honest comparison is not with other scrapers. It is with the alternative that this tool predates: a purpose-built image dataset. A scraper gives you an unbounded, current, free supply of images that match a keyword. That is exactly what you want when you are prototyping a classifier, testing a search idea, or gathering material to look at. It is also slow, because every image costs a browser-backed fetch, and it is not reproducible, because the same query returns different results tomorrow and a search page that changes layout changes your output with no warning. A curated dataset gives you the opposite profile: a fixed set of images with a fixed labelling scheme, a download that takes minutes rather than hours, and results that two people can compare. For training anything you intend to report on, or anything a reviewer will look at twice, that reproducibility is worth more than an endless supply. There is a middle option too, and it is what most careful users end up with: scrape for exploration, then record the specific images you kept into a checked-in manifest so the dataset can be rebuilt. The project's `tests/` and `docs/` directories suggest the code is maintained with some care, but nothing in the repository describes reproducible dataset versioning. The other comparison worth making is commercial image APIs, which return licensed images with predictable licensing terms. That is a different cost structure and often a different legal position, and it is the right comparison when the images end up in something you sell. ## What the repository tells you about maintenance The repository is not archived and the last push was on 2026-05-18. That is recent enough to say the code is not abandoned, but there are no published releases at all, which means there is no changelog to read and no version history to cite. For a scraper, the absence of releases is less alarming than it would be for a library, because the failure mode is not an API change. Google changes its pages, and the tool either still works or it does not. The Python version metadata drifting between `pyproject.toml` and `setup.py` suggests the packaging has not been revisited as carefully as the scraping logic. Two root files are worth noticing. `Licence.txt` sits at the top level, matching the MIT license declared in both build files, and `PUBLISHING.md` describes how releases are made, which is the closest thing to release documentation the project has. `CLAUDE.md` is also present, alongside a `.github/` directory. The topic list gives a good picture of what the maintainer thinks this is for: image datasets, image scraping, image processing, color filtering, a gallery, and the terminal. The keywords in `pyproject.toml` cover the same ground, including a small typo, since the keyword list has `image-scrapper` where the repository topics have `image-scraper`. That typo is harmless, but it is the kind of detail that tells you the packaging metadata and the repository metadata were last edited at different times. ## Conclusion The tool is a competent answer to a problem that quietly broke: image search moved behind JavaScript, so anything that used to parse HTML no longer works, and a browser-driven scraper is the straightforward response. It suits building a labelled image set for a prototype or for personal research, and the color filter plus the `--keywords` style arguments live behind a documented command line on the project's documentation site. It does not suit anything commercial: the README states that image copyright belongs to the original creators, and a dataset built this way carries that exposure with it. The last push was on 2026-05-18, there are no published releases, and the metadata in the repository disagrees with itself about supported Python versions, so verify `requires-python` against your environment before installing. ## FAQ ### Why can't I download from Google Images? The README's answer is that Google Images now requires JavaScript execution, so a plain HTTP request returns a page with no image URLs in it. This tool therefore uses Selenium with Chrome, and the README states that a Chrome or Chromium browser is required for it to work at all. ### Do I need to install Chrome to use google-images-download? Yes. The README requires Chrome or Chromium, and documents installing it per platform, with apt-get on Linux and a Homebrew cask on macOS, and a download from Google on Windows. Chromedriver is then handled automatically by webdriver-manager. ### What command does the package install? Installing it provides a console script named googleimagesdownload, declared in pyproject.toml as an entry point pointing at the main function inside the google_images_download package. The arguments it accepts are documented on the project's input arguments page rather than in the README. ### Can I use images downloaded this way commercially? The README says not to use any image that violates its copyright terms, states that copyright belongs to the original creators rather than to Google, and asks that the script be used only for educational purposes. It names cease-and-desist letters and lawsuits as the risk of reproducing copyrighted images without permission. ### Which Python versions does google-images-download support? The pyproject.toml file declares requires-python of 3.7 or later and lists classifiers for Python 3.7 through 3.12, while setup.py still carries older classifiers mentioning Python 2.7 and 3.3 through 3.6. The pyproject.toml declaration is the current one to trust. ## Sources - [hardikvasa/google-images-download on GitHub](https://github.com/hardikvasa/google-images-download) - [Issues](https://github.com/hardikvasa/google-images-download/issues) - [License: MIT](https://github.com/hardikvasa/google-images-download/blob/master/LICENSE) - [README](https://github.com/hardikvasa/google-images-download/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/hardikvasa-google-images-download