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README.md

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+---
+license: mit
+task_categories:
+  - image-to-text
+  - other
+tags:
+  - image-text
+  - deduplication
+  - perceptual-hash
+  - clip
+  - webdataset
+  - synthetic
+size_categories:
+  - n<1K
+pretty_name: Parallel Image-Text Dataset Builder (sample shard)
+---
+
+# parallel-image-text-dataset-builder (sample)
+
+A small representative sample from the
+[parallel-image-text-dataset-builder](https://github.com/narinzar/parallel-image-text-dataset-builder)
+pipeline: it ingests image-text pairs, removes near-duplicates with
+perceptual-hash (dhash) LSH-style bucketing, filters weak pairs by CLIP
+image-text similarity, and writes fixed-size WebDataset-style tar shards.
+
+## Contents
+
+- `shard-00002.tar` - one WebDataset-style shard (536 samples). Each sample is
+  two members sharing a key: `{key}.jpg` (image) and `{key}.txt` (caption).
+- `stats.json` - machine-readable statistics from the full run that produced this
+  sample.
+
+## Generation method
+
+Fully synthetic. Base images are procedural RGB patterns built from a sum of
+random low-frequency plane waves plus a bright blob and a per-image color tint,
+so each base image has a distinctive perceptual hash while a planted
+resize + JPEG-recompress copy hashes close to it. Captions are templated
+(`a {color} {shape} over a {scene}`) and only loosely tied to the abstract
+images, so absolute CLIP scores are low by construction.
+
+The published shard is the output of the full pipeline:
+
+1. Perceptual hashing (dhash, 9x8 grid, 64-bit) over all inputs.
+2. Near-duplicate removal via banded LSH bucketing + union-find (5-bit hamming
+   threshold, 8 bands).
+3. CLIP image-text similarity filtering (open_clip `ViT-B-32`
+   `laion2b_s34b_b79k`) at score threshold 0.10.
+4. Fixed-size tar sharding (1000 samples/shard).
+
+## Run this sample came from
+
+Measured on a single NVIDIA RTX 5090 (24 GB). Input: 3840 synthetic pairs
+(3000 unique base images + 840 planted near-duplicates).
+
+| stage | value |
+| ----- | ----- |
+| input pairs | 3840 |
+| after dedup | 3000 (removed 840: 637 exact, 203 near) |
+| dedup rate | 0.2188 (equals the planted duplicate fraction) |
+| after CLIP filter (threshold 0.10) | 2536 |
+| shards written | 3 (this repo ships shard 2, 536 samples) |
+| band pairs checked | 556,812 vs 7,370,880 all-pairs |
+
+This is a small-scale run that exercises the full path end to end; the numbers
+are real measurements, not a large-corpus benchmark.
+
+## Reading a shard
+
+```python
+import tarfile
+
+with tarfile.open("shard-00002.tar") as tar:
+    jpgs = [n for n in tar.getnames() if n.endswith(".jpg")]
+    print(len(jpgs), "images")
+```
+
+## License
+
+MIT.