What image/video training data is hardest to find right now? [R]

<!-- SC_OFF --><div class="md"><p>Public datasets on HF or Kaggle can sometimes be too generic, wrong domain, wrong schema, outdated, or just not enough volume to generalize

<!-- SC_OFF --><div class="md"><p>Trying to auto categorize a former employees photos from personal and work related. It’s a lot of photos and I don’t want the guy to loose

<!-- SC_OFF --><div class="md"><p>We've helped some folks with synthetic data for a number of different projects and some of them for &quot;document data&quot;. Like annotated

<!-- SC_OFF --><div class="md"><p>I created a simple text to image benchmark.</p> <p>I curated <strong>192 prompts that are difficult for T2I models</strong> in various ways: text

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1t0txco/why_is_table_extraction_with_vlm_models_still/"> <img alt="Why Is Table Extraction with VLM

<!-- SC_OFF --><div class="md"><p>I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data)

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve built a fairly flexible RAG pipeline that was initially designed to handle any type of document (PDFs, reports, mixed