[ READY FOR INPUT ]
Load image sets using subreddit name(s) above or paste your image from clipboard here (Ctrl+V).
Import individual images or batches directly into your active annotation session for dataset labeling and multimodal AI prompt analysis.
Your complete dataset will be compiled into a standardized ZIP archive ready for training with images, YOLO labels, COCO annotations, captions, and manifest. Ignored images are excluded.
labels/*.txt with normalized coords, data.yaml, and classes.txt.
Industry standard coco_annotations.json with bboxes & categories.
Individual *.txt prompt caption files paired with each image.
dataset.jsonl structured for multimodal vision-language fine-tuning.
Configure multimodal AI vision engines to generate descriptive captions covering poses, lighting, subjects, background, and cultural/regional characteristics.
Send a diagnostic test request to verify API key authentication, model availability, and response latency.
REDDIZ is a high-throughput dataset authoring workstation built for visual dataset generation, portrait training, and fine-tuning. It harvests Reddit image boards, formats training captions, bounds objects, and packages clean datasets.
Type target subreddits in the top bar (e.g. streetphotography, cats, wallpapers) and press
Enter. All media streams into the vertical filmstrip ready for annotation.
Use the inline model picker in the caption header or click ✦ PROMPT to analyze the image with Google Gemini, OpenAI ChatGPT, Anthropic Claude, or Groq Vision. The engine automatically outputs objective subject poses, facial features, Indian regional traits, lighting, shadows, and environment.
Click and drag directly on the canvas to draw class boxes (subject, foreground,
background). Add specific trigger tags or tokens with Enter.
Press [ENTER] to Save & Advance • [X] to Ignore • [↑ / ↓] or [A / D] to navigate. When done, click DONE & DOWNLOAD ZIP for training-ready YOLO, COCO, or Stable Diffusion archives.
An unexpected issue occurred while processing your request.
Snapshots include timestamps, image counts, annotations, and bounding boxes.