r/datasets

Uploaded 5.6 billion TikTok videos metadata on Hugging Face, spanning from 2014 to October 2026 [P]
▲ 98 r/datasets+1 crossposts

Uploaded 5.6 billion TikTok videos metadata on Hugging Face, spanning from 2014 to October 2026 [P]

Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos

If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Creators table - 4.5 billion rows
Videos table - 5.6 billion rows
Sounds table - 633 million rows

Comment below and I’ll DM you the database credentials.

Im self-hosting my database so please don't run heavy queries and crash my server.

u/DataShack — 7 days ago
▲ 11 r/datasets+5 crossposts

I launched 2 SaaS. First failed brutally. Second hit $5k MRR in 3 months. What worked and didn't.

tldr: My first AI SaaS failed because I built blindly, outsourced marketing, and had no clear ICP. My second product hit $5k/mo in 3 months because I built an audience first, obsessed over SEO and distribution, and targeted a specific niche.

I launched my first AI SaaS last year, kept it alive for about a year now. It's making less than $50 mrr and i'm thinking of closing it.

Here's what went wrong:

  • I just built what I thought was cool and useful without confirming any traction.
  • Biggest regret is outsourcing marketing and distribution. I wasn't an expert and thought outsourcing was the best way.
  • The product didn't have a clear ICP at all. I kept adding more and more features, hoping to get more users but it just led to confusion and reaching absolutely no one.
  • Didn't solve my own problem. I wasn't using it daily, which made me lose motivation, and that carelessness was reflected in the product.
  • I didn't prioritise SEO and GEO. Never really studied how it works, didn't think it was important.

For the second one, I changed everything.

SocialCrawl launched in April this year and it's already making $5k a month in revenue and en route to $10K this July.

Here is what actually worked this time around:

  • I figured out distribution and marketing before I even decided what to build.
  • I built an audience first, growing to 5,000+ followers on Threads and 1,500+ on X.
  • Being a developer myself, I chose to build an API dev tool that I could easily market to my own community.
  • I built something that solves my own problem and was also validated by talking to my ICPs and researching communities.
  • My ICP was specific (developers building something based on social media data) and I knew where they were hanging around
  • I focused heavily on SEO, posting blogs and building backlinks from day one.

Overall, failing the first time taught me you have to know who you're selling to, show up where your users hang out and be obsessed with distribution.

u/dooddyman — 8 days ago
▲ 11 r/datasets+2 crossposts

I took some pictures of a robot, high-specularity edge-case dataset (425 RAW/JPEGs) for benchmarking CV & depth-estimation algorithms against extreme mirror reflections

>Specular reflections, intense glare, and non-standard geometric silhouettes remain a major headache for computer vision, depth-estimation, and visual segmentation models—especially in outdoor robotics and autonomous navigation.
>
>To help test and benchmark how models handle severe optical disruption, we put together the Hitch Hiker 'Mirror Suit' Robotics Data archive.
>
>Dataset Overview:
>
>Primary Use Cases:
>
>The dataset is hosted on Mozilla Data Collective with gated access:
>
>Curious to hear how your current vision pipelines or depth cameras handle specular glare—what methods/filters are you using to prevent bounding box dropouts on highly reflective objects?

mozilladatacollective.com
u/5500kelvin — 8 days ago
▲ 2 r/datasets+1 crossposts

Looking for recommendations to purchase high-quality, production-grade PPE dataset (Helmet, Vest, Boots, Glasses, Gloves, Coverall)

Hi everyone,

We are currently building a commercial/production-grade Computer Vision system for industrial safety compliance. We need to acquire a comprehensive, well-annotated dataset covering the following classes:

  • Helmet / Hard Hat
  • Safety Vest
  • Safety Boots
  • Safety Glasses / Goggles
  • Safety Gloves
  • Coverall / Workwear

Requirements:

  • Quality: Commercial license, high resolution, diverse real-world industrial environments (construction, manufacturing, indoor/outdoor, varying lighting/weather).
  • Annotations: High-precision Bounding Boxes (or Segmentation Masks) with minimal label noise.
  • Scale: Looking for a solid base dataset (tens of thousands of annotated images) or a reliable data vendor/marketplace.

Public datasets (like standard Kaggle sets or Roboflow Universe) are great for prototyping, but we need high consistency and coverage for edge cases to reach production accuracy.

Questions:

  1. What are the best marketplaces or data vendors you’ve used for industrial/PPE datasets? (e.g., Scale AI, Appen, Dataloop, Dataset Ninja, etc.)
  2. Are there specialized computer vision data providers that offer pre-labeled industrial safety datasets?
  3. If you built a similar system, did you buy off-the-shelf data or custom-collect and outsource the annotation?

Any leads, recommendations, or warnings based on your experience would be greatly appreciated!

reddit.com
u/gavvy__ — 9 days ago