AI & ML interests

Deep Research , Multi-Agent Systems , Bio Informatics , Data Processing , Statistical Analyses , Medical Research

Recent Activity

dronefreak 
posted an update about 23 hours ago
view post
Post
95
🌧️❄️ Free demo: remove rain, raindrops, or snow from a photo with a single model

I put together an unofficial demo for **Histoformer** (ECCV 2024, arXiv: 2407.10172), a 16.6M-parameter transformer that handles three different weather degradations, rain streaks, adherent raindrops, and snow, in one unified model. It uses a "histogram self-attention" mechanism that groups pixels by degradation intensity instead of spatial position, which is a
neat way to sidestep the usual spatial-window tradeoffs in restoration transformers.

Try it here, free on ZeroGPU: dronefreak/histoformer-weather-restoration

Upload a photo and get a before/after slider. Two checkpoints available: one tuned for real-world photos, one for the paper's synthetic benchmarks.

Also put together a cleaner, easy-to-use model card with a copy-pasteable Quickstart if you'd rather run it yourself: dronefreak/Histoformer

This is an unofficial demo/mirror, not affiliated with the original authors. All credit for the actual research goes to Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, and Xiaochun Cao (@sunsean ). Official repo: https://github.com/sunshangquan/Histoformer. Weights are MIT-licensed.

Reported numbers from the paper: 32.1 PSNR on rain+fog (Outdoor-Rain), 33.1 on raindrops, 37.4 / 32.2 on light/heavy snow (Snow100K-S/L).
  • 2 replies
·
dronefreak 
posted an update 2 days ago
view post
Post
2096
🚀 Excited to open-source the SeaDronesSee Object Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on SeaDronesSee, spanning nano through x-large YOLO variants plus RF-DETR Nano/Small/Medium.
- 🌊 Benchmarked on SeaDronesSee's maritime search-and-rescue setting — swimmers, boats, jet skis, life-saving appliances and buoys captured by UAVs over open water, at varying altitudes and non-uniform image resolutions (1080p up to 4K+).
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 83.47% (RF-DETR Medium), 47.49% mAP@50:95, 87.01% precision.
- ⚡ Best efficiency tradeoff: YOLOv26s hits 80.14% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~3 points of the top RF-DETR variant, while actually beating YOLOv11x's 74.82% mAP@50 using ~8.6x fewer FLOPs (196.0 GFLOPs).

The goal is to make benchmarking and experimenting with maritime UAV perception easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench).

Full credit for the underlying dataset goes to Leon Amadeus Varga, Benjamin Kiefer, Martin Messmer, and Andreas Zell (University of Tübingen, WACV 2022) — this release is an unofficial, YOLO-ready reformatting of their work (CC0-licensed), not a new dataset.

If you're working on maritime search-and-rescue, UAV perception, autonomous drones, or real-time object detection, I hope these resources are useful.

📦 Dataset:
dronefreak/SeaDronesSee

🤖 Model Collection: dronefreak/seadronessee-object-detection-model-zoo-6a7b030a25797e5dd2d70123

Feedback, bug reports, and contributions are always welcome.
  • 2 replies
·
dronefreak 
posted an update 3 days ago
view post
Post
1895
🚀 Excited to open-source the GWHD Wheat Head Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on GWHD (Global Wheat Head Dataset), spanning nano through x-large variants across both architecture families.
- 🌾 Benchmarked on GWHD's dense, single-class wheat-head detection task — ~45 annotated heads per image on average, captured across multiple countries, genotypes, and growth stages, a genuinely hard small/dense-object setting.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 74.25% (YOLOv11x), 34.92% mAP@50:95, 83.37% precision.
- ⚡ Best efficiency tradeoff: YOLOv26s hits 70.49% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~4 points of the top YOewer FLOPs (196.0 GFLOPs).

The goal is to make benchmarking and experimenting with agricultural computer vision easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench).
Full credit for the underlying dataset goes to Etienne David, Mario Serouart, Simon Madec, and the Global Wheat Head Detection 2020/2021) — this release is anunofficial, YOLO-ready reformatting of their work, not a new dataset.

If you're working on precision at detection, or just want areproducible detector benchmark, I hope these resources are useful.

📦 Dataset:
dronefreak/GWHD

🤖 Model Collection: dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1

Feedback, bug reports, and contributions are always welcome.
  • 3 replies
·
dronefreak 
posted an update 12 days ago
view post
Post
929
🚀 Excited to open-source the VDD Semantic Segmentation Model Zoo on Hugging Face.

This release includes:

- 🤖 CABiNet and YOLO26 semantic segmentation models trained on VDD (Varied Drone Dataset), spanning Nano through XLarge YOLO26 variants plus a CABiNet (MobileNetV3-Large) baseline.
- 🌍 Benchmarked on VDD's varied altitudes, viewpoints, and scenes (urban, rural, natural) — a more diverse and challenging setting than single-flight UAV footage.
- 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative RGB / Ground-Truth / Prediction comparisons, and training configurations for reproducibility.

Headline numbers:
- 🏆 Best mIoU: 78.83% (YOLO26x-sem)
- ⚡ Best efficiency tradeoff: CABiNet-Large hits 77.76% mIoU at just 54.8 GFLOPs — within 1-2 points of the top YOLO26 variantO26x's 430.9 GFLOPs)

The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use pretraineat, all trained and evaluatedunder one shared pipeline.

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

📦 Dataset: RussRobin/VDD

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

Feedback, bug reports, and contributions are always welcome.
dronefreak 
posted an update about 1 month ago
view post
Post
4555
🚀 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.
dronefreak 
posted an update about 2 months ago
view post
Post
3338
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
·
Tonic 
posted an update 3 months ago
view post
Post
3225
🙋🏻‍♂️ Hey there folks ,

Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.

Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.

meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .

I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.

At least that's the concept !

check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth


- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
  • 2 replies
·
Tonic 
posted an update 4 months ago
view post
Post
4422
🙋🏻‍♂️ Hey there folks,

since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !

Check this one out :
NuTonic/sat-bbox-metadata-sft-v1

the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .

hope you like it ! 🚀
  • 2 replies
·
Tonic 
posted an update 4 months ago
view post
Post
3722
🙋🏻‍♂️ Hey there folks ,

I'm sharing huggingface's largest dataset of annotated statelite images today.

check it out here : NuTonic/sat-image-boundingbox-sft-full

I hope you like it , the idea is to be able to use this with small vision models 🚀
Tonic 
posted an update 6 months ago
view post
Post
3855
🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named
kurakurai
?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
  • 4 replies
·
Tonic 
posted an update 6 months ago
view post
Post
3496
🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest


just a few quite minor details i'll take care of but i wanted to share here first
  • 2 replies
·
Tonic 
posted an update 11 months ago
Tonic 
posted an update 11 months ago
view post
Post
865
COMPUTER CONTROL IS ON-DEVICE !

🏡🤖 78 % of EU smart-home owners DON’T trust cloud voice assistants.

So we killed the cloud.

Meet Exté: a palm-sized Android device that sees, hears & speaks your language - 100 % offline, 0 % data sent anywhere.

🔓 We submitted our technologies for consideration to the Liquid AI hackathon.

📊 Dataset: 79 k UI-action pairs on Hugging Face (largest Android-control corpus ever) Tonic/android-operator-episodes

⚡ Model: 98 % task accuracy, 678MB compressed , fits on existing android devices ! Tonic/l-android-control

🛤️ Experiment Tracker : check out the training on our TrackioApp Tonic/l-android-control

🎮 Live Model Demo: Upload an Android Screenshot and instructions to see the model in action ! Tonic/l-operator-demo



Built in a garage, funded by pre-orders, no VC. Now we’re scaling to 1 k installer units.

We’re giving 50 limited-edition prototypes to investors , installers & researchers who want to co-design the sovereign smart home.

👇 Drop “EUSKERA” in the comments if you want an invite, tag a friend who still thinks Alexa is “convenient,” and smash ♥️ if AI should belong to people - not servers.
  • 4 replies
·
Tonic 
posted an update 11 months ago
view post
Post
790
🙋🏻‍♂️ Hey there folks ,

Just wanted to annouce 🏭SmolFactory : it's the quickest and best way to finetune SmolLM3 and GPT-OSS-20B on huggingface !

Basicaly it's an app you can run on huggingface by duplicating the space and running your training directly on huggingface GPUs .

It will help you basically select datasets and models, fine tune your model , make an experiment tracker you can use on your mobile phone , push all your model card and even automatically make a demo for you on huggingface so you can directly test it out when it's done !

check out the blog to learn more : https://huggingface.co/blog/Tonic/smolfactory

or just try the app directly :
Tonic/SmolFactory

you can vibe check the cool models I made :
French SmolLM3 : Tonic/Petite-LLM-3
Medical GPT-OSS : Tonic/med-gpt-oss-20b-demo

check out the model cards :
multilingual reasoner (gpt-oss) - Tonic/gpt-oss-20b-multilingual-reasoner
med-gpt-oss : Tonic/med-gpt-oss-20b
petite-elle-l-aime : Tonic/petite-elle-L-aime-3-sft

github repo if you like command line more than gradio : https://github.com/josephrp/smolfactory

drop some likes on these links it's really much appreciated !

feedback and PRs are welcome !
Tonic 
posted an update about 1 year ago