🤝 Open to Collab

Saumya Saksena

dronefreak

19 1 5

https://scholar.google.com/citations?user=BxQ0KDEAAAAJ&hl=en

AI & ML interests

Computer Vision, Deep Learning, Image Restoration, Image De-noising, LLMs, RAG, Image Classification, Image Segmentation, EEG Classification, Signal Processing, PPG, BCI

Recent Activity

updated a collection 2 days ago

UAVid Semantic Segmentation Model Zoo

updated a collection 2 days ago

UAVid Semantic Segmentation Model Zoo

new activity 2 days ago

dronefreak/cabinet-mobilenetv3-large-uavid:Upload 6 files

View all activity

Organizations

posted an update 7 days ago

Post

4351

🚀 Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face.

This release includes:

* 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines.
* 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility.

The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format.

If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.

**📦 Dataset:** dronefreak/UAVid-2020

**🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo

Feedback, bug reports, and contributions are always welcome.

reacted to AbstractPhil's post with 🔥 7 days ago

Post

4142

Massive AlephLM success. The task collective is producing powerful MOE shared knowledge adapters. A serious success and a massive first step towards the next stage. The current family collective results are present here; AbstractPhil/geolip-aleph-qwen

This is akin to a stackable non-intrusive lora that enables increased shared collective behavior.

This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees.

All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process.

The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network.

Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.

4 replies

replied to their post 15 days ago

I have updated all licenses to AGPL-3 which are the ones used by the original YOLO models.

replied to their post 19 days ago

Thanks for your kind words, and I am glad you found the models useful. You are right to remark about the yolo26-nano performance. I will be starting shortly trying to distill a yolov8s from yolo26-x, in the hopes of further improving the performance.

Also, thanks for your licensing remarks, I will update the licenses of the model cards to AGPL to follow the upstream model licenses.

replied to their post 21 days ago

If you can give me your email id, I can add you to discord or some other form of communication channel.

replied to pankajpandey-dev's post 23 days ago

Aag the fire!

reacted to pankajpandey-dev's post with 🔥 23 days ago

Post

7828

🇮🇳 New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi — and it runs on your laptop's CPU.
I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU.
Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting 👇
✅ My fine-tune is more concise — ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.

✅ Pure native Hindi — base keeps slipping into English ("संतुलित आहार (Eat a Balanced Diet)", "तारा (Star)"). My fine-tune stays in clean Hindi.

✅ Tighter instruction-following — ask for a "short message" and it gives one, not a menu of options.
⚖️ And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model — I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU.
🔗 Try it:

Live demo (CPU): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct

Built with @unsloth · Data by @ai4bharat 🙏
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF

12 replies

replied to their post 23 days ago

I am glad to know you are interested in this work. I am going to make an initial plan and share it with you ASAP. We can collaborate on GitHub. It will be nice to have some help on this as I myself dont have a lot of time often.

Thanks!

replied to their post 29 days ago

Hi Masud,

Thank you for your kind words. I will shortly start working on DOTA Aerial Detection Benchmark to evaluate the YOLO variants on this aerial dataset. We can collaborate on the development part if you want (although I might be able to work on this only on the weekends).

reacted to their post with 🔥 about 1 month ago

Post

3239

Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.

The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.

If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.

Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo

Feedback, issues, and contributions are welcome.

13 replies

replied to their post about 1 month ago

Hi,

So what I understood from the paper is that pedestrian is classified as a type of person without any accessory like a bike or a scooter etc. Person/people according to the dataset are the ones on the roads on sidewalks on some kind of transport vehicles. Even though the authors did not specify this distinction, its what I understood.

posted an update about 1 month ago

Post

3239

13 replies

Saumya Saksena

AI & ML interests

Recent Activity

Organizations

dronefreak's activity