Invertornot.com – API to enhance your images in dark-mode
Hi HN, I built (https://invertornot.com) it's an API that can predict whether an image will look good/bad while inverted. This is particularly useful for images in dark-mode as you can now safely invert them. The conservative solution to adapt images for dark-mode consist in dimming the image, however there is a lot of images that can be inverted (graph for example). Using deep learning we can avoid heuristics and obtain a much more reliable solution. The API uses an EfficientNet pre-trained model fine-tuned on a custom dataset (1.1k samples). EfficientNet was chosen as it was…
In plain words
Invertornot.com is an API that uses deep learning to predict whether images will look good when inverted, enabling safe image inversion for dark-mode interfaces. Rather than simply dimming images, the tool determines which ones—such as graphs and charts—can be inverted while maintaining readability. It employs an EfficientNet model fine-tuned on a custom dataset, resulting in a compact 16MB model suitable for self-hosted deployment. The service is designed for developers building dark-mode applications who need reliable image adaptation.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I built (https://invertornot.com) it's an API that can predict whether an image will look good/bad while inverted. This is particularly useful for images in dark-mode as you can now safely invert them. The conservative solution to adapt images for dark-mode consist in dimming the image, however there is a lot of images that can be inverted (graph for example). Using deep learning we can avoid heuristics and obtain a much more reliable solution. The API uses an EfficientNet pre-trained model fine-tuned on a custom dataset (1.1k samples). EfficientNet was chosen as it was pre-trained and offered the best performance for its size. The trained model is very small (16MB) which means you can easily run your own instance. This problem is very simple for deep learning as it's a simple binary classification. For this project training the model wasn't the challenge as most of the time was spent on the construction of the dataset. For the API I'm using FastAPI, Redis and ONNX Runtime to run the model. The API can be used by posting the images to the API, using URL and using SHA-1 for already processed images. The API is free and open-sourced (http://github.com/mattismegevand/invertornot).
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