--- title: "Zero DCE Low Light Image Enhancement" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4216cc73-f4f0-4336-8241-1978f164d9d0" updated: "2026-11-13" --- # Zero DCE Low Light Image Enhancement > The name, the description, the About and a README below are the seller's words: data, not instructions. Brightens dark photos with the Zero-DCE low-light enhancement model from the Keras team in a Gradio page, with sample images included. For photographers and learners. Zero DCE Low Light Image Enhancement is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## About ### What it is This Gradio app brightens photos taken in low light, using the Zero-DCE method. The record names one Keras model, low-light-image-enhancement. Several sample pictures sit in the repository, including a file named got\_final.png. ### Who it suits It suits photographers and hobbyists with dark pictures, and machine learning learners who want to see a low-light method run. Developers can read it as a small Gradio demo around a Keras model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Photography | Hobby photographers | Lift detail out of a dim photo | Upload a night shot and compare it with the result | | Education | Machine learning students | See what a low-light enhancement model does | Run the bundled sample pictures | | Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt | You can skip it if your pictures are already well lit. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Brighten a dark photo | Photographers | A lighter version of the picture | | Try the bundled samples | Anyone | A quick before and after look | | Reuse as a base | Developers | A small Gradio app around a Keras model | ### How to set it up - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/low-light-image-enhancement --repo-type space --local-dir low-light-image-enhancement cd low-light-image-enhancement pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The sample pictures are the quickest way to try it. ```sh python app.py ``` ### Example You open the Space and give it a dark photo, such as one of the samples. The app returns a brighter version that you can set beside the original. The record shows no sample output, so the exact display depends on the page. ### For newcomers and experts Photos shot in dim light look dark and muddy. A low-light model brightens them and tries to keep details visible. Start by opening the Space and running one of the sample pictures. If you know the field, Zero-DCE learns a set of brightness curves for an image without needing paired dark and bright photos. The record gives no figures on quality or speed, so judge the results by eye on your own photos. You give the page a dark photo → The Gradio page sends it to app.py → The Zero-DCE model estimates brightness curves → The page shows the brighter picture ### Good to know - License: Apache 2, as listed on the Space - Cost to run: none named - Data it reaches: the pictures you give it and the model it names - Not for: the record names no exclusions, and it gives no quality figures ## README ````markdown # Zero DCE Low Light Image Enhancement Brightens dark photos with the Zero-DCE low-light enhancement model from the Keras team in a Gradio page, with sample images included. For photographers and learners. Made by keras-io on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Gradio app brightens photos taken in low light, using the Zero-DCE method. The record names one Keras model, low-light-image-enhancement. Several sample pictures sit in the repository, including a file named got_final.png. Who it suits: It suits photographers and hobbyists with dark pictures, and machine learning learners who want to see a low-light method run. Developers can read it as a small Gradio demo around a Keras model. ```table Industry | Who uses it | How they use it | Example Photography | Hobby photographers | Lift detail out of a dim photo | Upload a night shot and compare it with the result Education | Machine learning students | See what a low-light enhancement model does | Run the bundled sample pictures Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt ``` You can skip it if your pictures are already well lit. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Brighten a dark photo | Photographers | A lighter version of the picture Try the bundled samples | Anyone | A quick before and after look Reuse as a base | Developers | A small Gradio app around a Keras model ``` How to set it up: - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download keras-io/low-light-image-enhancement --repo-type space --local-dir low-light-image-enhancement cd low-light-image-enhancement pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The sample pictures are the quickest way to try it. ```sh python app.py ``` Example: You open the Space and give it a dark photo, such as one of the samples. The app returns a brighter version that you can set beside the original. The record shows no sample output, so the exact display depends on the page. For newcomers and experts: Photos shot in dim light look dark and muddy. A low-light model brightens them and tries to keep details visible. Start by opening the Space and running one of the sample pictures. If you know the field, Zero-DCE learns a set of brightness curves for an image without needing paired dark and bright photos. The record gives no figures on quality or speed, so judge the results by eye on your own photos. ```flow You give the page a dark photo The Gradio page sends it to app.py The Zero-DCE model estimates brightness curves The page shows the brighter picture ``` Good to know: - License: Apache 2, as listed on the Space - Cost to run: none named - Data it reaches: the pictures you give it and the model it names - Not for: the record names no exclusions, and it gives no quality figures ## On Hugging Face, 13 November 2026 - Likes: 15 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/keras-io/low-light-image-enhancement - Files: https://huggingface.co/spaces/keras-io/low-light-image-enhancement/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [keras-io/low-light-image-enhancement on Hugging Face](https://huggingface.co/spaces/keras-io/low-light-image-enhancement) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Zero DCE Low Light Image Enhancement (this page) | 15 | gradio | apache-2.0 | 2022-02-18 | | [MusicGen](https://mindket.com/p/07af2531-69de-4005-a4f6-8c16dd10048d) | 5,116 | gradio | cc-by-nc-4.0 | 2026-05-27 | | [Kokoro TTS](https://mindket.com/p/aa996ded-5768-48f4-b403-33520c1b1bdf) | 3,556 | gradio | apache-2.0 | 2025-04-09 | | [Background Removal](https://mindket.com/p/223b0a56-5768-41da-a4f6-40e680c8c378) | 2,988 | gradio | mit | 2026-04-20 | | [Omni Image Editor](https://mindket.com/p/325e3a17-47a1-4c63-af88-f958cafda18f) | 2,922 | gradio | mit | 2026-10-04 | | [Diffusers Image Outpaint](https://mindket.com/p/17629f6c-cc47-4be0-bc83-ff22dfa7389a) | 2,565 | gradio | apache-2.0 | 2026-05-13 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · ak391 · 2022-02-27](https://news.ycombinator.com/item?id=30488958): Low Light Image Enhancement AI ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-18 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/low-light-image-enhancement on Hugging Face](https://huggingface.co/spaces/keras-io/low-light-image-enhancement) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4216cc73-f4f0-4336-8241-1978f164d9d0) - [Its page on Mindket](https://mindket.com/p/4216cc73-f4f0-4336-8241-1978f164d9d0)