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十方空烬

WideChaos
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dipankarsarkar's profile picture
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https://www.widechaos.cn

AI & ML interests

I build LLM applications and agent systems, backed by experience in backend engineering, distributed storage and cloud infrastructure. My interests span data pipelines, model training and fine-tuning, evaluation, inference, deployment and full-stack AI products. My public projects include Evidence Agent and Bilingual Patch Contracts. I also publish small synthetic experiments: a bilingual routing LoRA and JSON Repair Boundary Audit. I share reproducible examples, inspectable tool traces and failure analysis.

Recent Activity

updated a dataset about 8 hours ago
WideChaos/rejected-check-audit
published a dataset about 8 hours ago
WideChaos/rejected-check-audit
repliedto Parveshiiii's post 4 days ago
Most AI image detectors work fine in the lab and fail on social media. The reason is simple. Real photos get compressed, resized and re-uploaded again and again, and detectors trained on clean images read that damage as a sign of fakery. A real photo that has passed through a few platforms often gets flagged as AI-generated. AIRealNet-2 is built to fix that. It is the upgrade to our original https://huggingface.co/Modotte/AIRealNet, trained on images collected from platforms like Instagram and X, so it has seen the kinds of quality loss and compression real images go through online. The training set has 98k images, split 50/50 between AI-generated and real, so the model has no class bias to lean on. Personal photos were excluded by design, including AI-edited ones. The architecture is unchanged: a SwinV2 Tiny backbone with a binary classification head, about 0.2B parameters, working at 256x256 resolution. It stays fast and easy to deploy for research or production. Model: https://huggingface.co/Modotte/AIRealNet-2
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