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Parveshiiii 
posted an update 1 day ago
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Most NSFW classifiers break the second an image touches the internet.

They look great on pristine benchmarks, but in the wild, every social platform aggressively recompresses, downsamples, and degrades images.
The moment JPEG or WebP compression artifacts show up, confidence collapses and false positives spike.

SafeScan was built to survive actual platform pipelines.
Trained on 34,000 images under almost every major social media compression profile using a Vision Transformer backbone (google/vit-base-patch16-224). Instead of blunt binary filtering, it breaks decisions down across 5 clear categories:

• safe
• drawing
• sexy
• hentai
• porn

The result is a moderation model that actually generalizes to real-world internet feeds instead of fragile, uncompressed datasets.
Open-weight and available on Hugging Face:

Model: Parveshiiii/SafeScan
Parveshiiii 
posted an update 4 days ago
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3804
Most deepfake audio detectors are quietly cheating.

They don’t really listen to the speech — they just look at how long the embedding vector is. Once they figure that out, accuracy looks great on paper and falls apart in the wild.

AIRealNet-Audio was built to stop that shortcut.
It forces every feature onto the unit hypersphere (twice) so the model can only use direction, not magnitude. Trained on speech from 100+ different TTS and voice-cloning systems, plus real human recordings under heavy compression and noise.

The result is a detector that actually has to learn the artifacts instead of gaming the feature space.

Model: Modotte/AIRealNet-Audio
satpalsr 
posted an update 2 months ago
satpalsr 
posted an update 3 months ago
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1947
We just released an end-to-end system that sets a new state of the art in egocentric video understanding, generating fine-grained action labels from raw robot and human videos while outperforming Gemini, GPT, Claude, and other leading models.

https://x.com/fpv_labs/status/2079600323883331880
johko 
posted an update 5 months ago
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248
One prompt, three answers - which model is from where?

johko/llm-blind-date

I built a little demo where you give three models (Apertus, Llama, Qwen3) the same prompt and in the end you have to guess which is which just based on their answers.

GIve it a try! ;)
satpalsr 
posted an update 5 months ago
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We're open-sourcing our infra with 10M+ frames of dataset!

We're releasing Stera, an open-source infra that turns an off-the-shelf device in your pocket into a high-fidelity multimodal data pipeline. It's built around four layers. Capture → Process → Evaluate → Export.

Stera Capture removes the need for bespoke/gated hardware and runs on an off-the-shelf iPhone. It fuses together synchronized RGB, IMU, Lidar-guided depth, and 6-DoF pose out of the box from ARKit and exports them to a raw MCAP file.

Dataset: fpvlabs/stera-10m
Launch Details: https://x.com/fpv_labs/status/2055262652033908832
Ujjwal-Tyagi 
posted an update 5 months ago
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6 Open-Source Libraries to FineTune LLMs
1. Unsloth
GitHub: https://github.com/unslothai/unsloth
→ Fastest way to fine-tune LLMs locally
→ Optimized for low VRAM (even laptops)
→ Plug-and-play with Hugging Face models

2. Axolotl
GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
→ Flexible LLM fine-tuning configs
→ Supports LoRA, QLoRA, multi-GPU
→ Great for custom training pipelines

3. TRL (Transformer Reinforcement Learning)
GitHub: https://github.com/huggingface/trl
→ RLHF, DPO, PPO for LLM alignment
→ Built on Hugging Face ecosystem
→ Essential for post-training optimization

4. DeepSpeed
GitHub: https://github.com/microsoft/DeepSpeed
→ Train massive models efficiently
→ Memory + speed optimization
→ Industry standard for scaling

5. LLaMA-Factory
GitHub: https://github.com/hiyouga/LLaMA-Factory
→ All-in-one fine-tuning UI + CLI
→ Supports multiple models (LLaMA, Qwen, etc.)
→ Beginner-friendly + powerful

6. PEFT
GitHub: https://github.com/huggingface/peft
→ Fine-tune with minimal compute
→ LoRA, adapters, prefix tuning
→ Best for cost-efficient training
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Ujjwal-Tyagi 
posted an update 6 months ago
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This is the best set of AI and ML books and a full guide to learning machine learning from the ground up. This is my study material that I used, so I thought it would be helpful to share it with others. Like, share, and add it to your collection at Ujjwal-Tyagi/ai-ml-foundations-book-collection.
Ujjwal-Tyagi 
posted an update 6 months ago
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3986
We are hiring at Shirova AI. We need AI researchers and engineers to work in our research lab. Shirova AI is a research lab in India, so we can help our researchers move to nearby workspaces or let them work from home without ever coming to the lab. We're building our founding team, so the pay will be good. You can learn, so don't hesitate to mail us at: careers@shirova.com
Parveshiiii 
posted an update 6 months ago
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686
🚀 Sonic: A lightweight Python audio processing library with tempo matching, BPM detection, time-stretching, resampling & track blending — now with GPU (CUDA) acceleration for 10x speed!

Perfect for quick remixes, batch edits or syncing tracks.

👉 https://github.com/Parveshiiii/Sonic

#Python #AudioProcessing #OpenSource #PyTorch
satpalsr 
posted an update 6 months ago
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OpenAI is hiring for SLAM Engineers!
And open-source shouldn't lag behind.

It's pretty hard and necessary problem required to be solved for bringing generalisable robots in real-world.

We are pushing out first deep down & will be open-sourcing stuff in the next releases. Hope everyone is ready! Cheers to HF & more hugs.

Find us at https://x.com/fpv_labs/status/2042585804162371713
Parveshiiii 
posted an update 6 months ago
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Excited to announce my latest open-source release on Hugging Face: Parveshiiii/breast-cancer-detector.

This model has been trained and validated on external datasets to support medical research workflows. It is designed to provide reproducible benchmarks and serve as a foundation for further exploration in healthcare AI.

Key highlights:
- Built for medical research and diagnostic study contexts
- Validated against external datasets for reliability
- Openly available to empower the community in building stronger, more effective solutions

This release is part of my ongoing effort to make impactful AI research accessible through **Modotte**. A detailed blog post explaining the methodology, dataset handling, and validation process will be published soon.

You can explore the model here: Parveshiiii/breast-cancer-detector

#AI #MedicalResearch #DeepLearning #Healthcare #OpenSource #HuggingFace

Ujjwal-Tyagi 
posted an update 6 months ago
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I am sharing my study material for AI & ML, these books are really a "bible" and gives very strong foundation, I also have given guidance, introduction and my master notes in the dataset repo card! I hope you will find them helpful, if you have any queries, just start a discussion and I am always there to help you out!
Ujjwal-Tyagi/ai-ml-foundations-book-collection
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