VDrontGPT50m
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Mihail
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SupraLabs/SupraCMA-8MOrganizations
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Ei, para que serve este modelo?
1
#1 opened about 13 hours ago
by 
RexTRO111
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reacted to dronefreak's post with π§ about 14 hours ago
Post
1041
π Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face.
This release includes:
* π¦ 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.
* π€ Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* π 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.
**π¦ Dataset:** dronefreak/UAVid-2020
**π€ Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
Feedback, bug reports, and contributions are always welcome.
This release includes:
* π¦ 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.
* π€ Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* π 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.
**π¦ Dataset:** dronefreak/UAVid-2020
**π€ Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
Feedback, bug reports, and contributions are always welcome.
Idea do modelo
π 3
12
#3 opened 1 day ago
by RexTRO111
liked 2
datasets about 15 hours ago
reacted to Quazim0t0's post with π€―β€οΈ about 16 hours ago
Post
2669
πΌ DaisyChain-Web: train a language model with friends or by yourself with multiple devices, in the browser, no install
Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.
What's actually happening under the hood:
π§ A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
β‘ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3ΓINT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
π Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
π Live logs show exactly what every device contributes, step by step
πΎ When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.
π Try it: Quazim0t0/DaisyChain-Web
π Training framework: DaisyChainAI/DaisyChain-Train
Proof of concept: only train with devices you trust. Feedback welcome!
Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.
What's actually happening under the hood:
π§ A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
β‘ Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3ΓINT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
π Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
π Live logs show exactly what every device contributes, step by step
πΎ When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.
π Try it: Quazim0t0/DaisyChain-Web
π Training framework: DaisyChainAI/DaisyChain-Train
Proof of concept: only train with devices you trust. Feedback welcome!
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