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license: mit |
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# 🗿 Megalith-10m |
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### What is Megalith-10m? |
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Megalith-10m is a dataset of ~10 million links to Flickr images that were categorized as "photo" with [license info](https://www.flickr.com/services/api/flickr.photos.licenses.getInfo.htm) of: |
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* [No known copyright restrictions (Flickr commons)](https://www.flickr.com/commons/usage), or |
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* [United States Government Work](https://en.wikipedia.org/wiki/Copyright_status_of_works_by_the_federal_government_of_the_United_States), or |
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* [Public Domain Dedication (CC0)](https://creativecommons.org/publicdomain/zero/1.0/), or |
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* [Public Domain Mark](https://en.wikipedia.org/wiki/Public_Domain_Mark) |
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### What's the intended use of Megalith-10m? |
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Megalith-10m is intended to contain only links to wholesome unedited uncopyrighted photographs - the sort of images that we humans see when we walk around outside. |
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I collected Megalith-10m for the purpose of training neural networks, but you're welcome to use Megalith-10m for whatever you want. |
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Of course, I recommend conducting your own independent analysis of content and copyright status before using Megalith-linked images in Serious Projects. |
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### Where can I get text captions for Megalith-10m? |
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* [DrawThings.ai](https://drawthings.ai) have uploaded [`megalith-10m-sharecap`](https://huggingface.co/datasets/drawthingsai/megalith-10m-sharecap) (captions made with [ShareCaptioner](https://huggingface.co/Lin-Chen/ShareCaptioner)) <br/><a href="https://huggingface.co/datasets/drawthingsai/megalith-10m-sharecap"><img src='https://cdn-uploads.huggingface.co/production/uploads/630447d40547362a22a969a2/vXM-x4TNfRn3AQTRGveLn.png' width=720px/></a> |
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* [AI Picasso](https://aipicasso.app) have uploaded [`megalith-10m-florence2`](https://huggingface.co/datasets/aipicasso/megalith-10m-florence2) (captions made with [Florence 2](https://huggingface.co/microsoft/Florence-2-large)) <br/><a href="https://huggingface.co/datasets/aipicasso/megalith-10m-florence2"><img src='https://cdn-uploads.huggingface.co/production/uploads/630447d40547362a22a969a2/RVHZluYqq4-pB1mFpq5Qj.png' width=720px/></a> |
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* [CaptionEmporium](https://huggingface.co/CaptionEmporium) have uploaded [`flickr-megalith-10m-internvl2-multi-caption`](https://huggingface.co/datasets/CaptionEmporium/flickr-megalith-10m-internvl2-multi-caption) (captions made with [InternVL2-8B](https://huggingface.co/datasets/CaptionEmporium/flickr-megalith-10m-internvl2-multi-caption/blob/main/OpenGVLab/InternVL2-8B) as well as shorter single-sentence captions made by summarizing the InternVL2/Florence2/ShareCaptioner results with [Llama3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)) <br/><a href="https://huggingface.co/datasets/CaptionEmporium/flickr-megalith-10m-internvl2-multi-caption"><img src='https://cdn-uploads.huggingface.co/production/uploads/630447d40547362a22a969a2/BObamPthy8kiQICGjCQ4f.png' width=720px/></a> |
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* [DrawThings.ai](https://drawthings.ai) is [working on](https://x.com/liuliu/status/1816318789795078307) further captioning with [MoonDream2](https://moondream.ai) |
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### How can I efficiently download the images referenced by Megalith-10m? |
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* [DrawThings.ai](https://drawthings.ai) has archived the images linked by Megalith-10m here: https://huggingface.co/datasets/drawthingsai/megalith-10m |
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* If you want to download Megalith-10m images directly from Flickr, I posted a sample [downloading command](https://huggingface.co/datasets/madebyollin/megalith-10m/discussions/2#6693f3a7e05c3f1e0e0d62c1) you can use with [img2dataset](https://github.com/rom1504/img2dataset/) |
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### How was Megalith-10m collected? |
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I used the Flickr API to query for photos matching some basic criteria (SFW photo with CC0 / public domain license info), which gave me around 12 million links. |
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I then used various filtering strategies to exclude ~2m image links which didn't appear to point to wholesome public-domain minimally-edited photos. |
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These filtering strategies included: |
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1. Account-level filtering, based on |
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1. Manual adjudication for the top 5000 most prolific accounts |
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2. Repeated-watermark detection |
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2. Photo-level filtering, based on |
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1. Image metadata |
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1. Mention of copyright restrictions in the EXIF tags |
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2. Mention of copyright restrictions in the text description |
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2. Image content |
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1. Duplicate detection |
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2. CLIP-assisted checking for |
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1. Clearly non-photo images (illustrations, screenshots, 3d renders, etc.) |
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2. Clearly non-wholesome images (violence, nudity, etc.) |
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3. Minimum-resolution enforcement (at least 256x256 pixels) |
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4. Manual spot-checking of some images and metadata |
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### What content does Megalith-10m contain? |
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The [demo notebook](./Megalith_Demo_Notebook.ipynb) shows a random sample of 100 images being loaded from the links in Megalith-10m. |
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Based on this random sample, I would estimate the following dataset statistics: |
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* 5-7% of images may have minor edits or annotations (timestamps, color grading, borders, etc.) |
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* 1-2% of images may be copyright-constrained (watermarks or text descriptions cast doubt on the license metadata) |
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* 1-2% of images may be non-wholesome (guns, suggestive poses, etc.) |
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* 1-2% of images may be non-photos (paintings, screenshots, etc.) |
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### Is 10 million images really enough to teach a neural network about the visual world? |
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For the parts of the visual world that are well-represented in Megalith-10m, definitely! |
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Projects like [CommonCanvas](https://arxiv.org/abs/2310.16825), [Mitsua Diffusion](https://huggingface.co/Mitsua/mitsua-diffusion-one), and [Matryoshka Diffusion](https://arxiv.org/abs/2310.15111) |
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have shown that you can train useable generative models on similarly-sized image datasets. |
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Of course, many parts of the world aren't well-represented in Megalith-10m, so you'd need additional data to learn about those. |
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### What have people done with Megalith-10m? |
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1. AI Picasso have successfully trained a full text-to-image model [CommonArt β](https://huggingface.co/aipicasso/commonart-beta) on Megalith-10m (and other open datasets). |
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2. I've successfully trained small [text-to-image models](https://x.com/madebyollin/status/1788282620981497981) on Megalith-10m for my own education. |
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3. Megalith-10m was among the datasets used to train [Janus](https://github.com/deepseek-ai/Janus), DeepSeek's AR model for multimodal understanding and generation |
